literature review on 3d printing technology

Organizational adoption of 3D printing technology: a semisystematic literature review

Journal of Manufacturing Technology Management

ISSN : 1741-038X

Article publication date: 10 December 2020

Issue publication date: 17 December 2021

Three-dimensional (3D) printing (3DP) offers a promising value proposition across multiple manufacturing industries. Despite the variety of production benefits the technology entails, its rate of adoption is still low compared to industry forecasts. In face of this challenge, industry as well as academia requires more information and guidance. This review aims to examine the characteristics of the existing body of research on the organizational adoption of 3DP as well as its underlying theoretical concepts. The most common criteria driving adoption will be derived, such as to facilitate the managerial decision-making process. Pathways for future research will be presented.

Design/methodology/approach

This study underlies a bibliometric literature review and additionally applies content analysis to systematically investigate the existing body of research and group decision criteria along the four major pillars of strategic decision-making.

The contributions of this paper are threefold. First, the bibliometric analysis reveals interesting aspects of the existing body of research. The most prominent characteristics of the contemporary literature are reflected along descriptive indicators, such as industry, method, model, origin, research outlet or adoption drivers, thus granting relevant insights into academia and practice. Second, the most notable adoption models are carefully analyzed on their inherent attributes and their application fit for the context of organizational 3DP adoption. Findings, for instance, revealed the dominance of diffusion of innovation (DOI) across the existing body of research and divulge that this construct is generally applied in combination with user-centered decision frameworks to yield more precise results. Third, an ample range of opportunities for future research are detected and thoroughly explained. Among others, the authors identified a clear lack of information on the impact environmental variables and contingency factors exerted on the organizational adoption of 3DP. Guidance in relation to the sourcing of industry data, usage of adoption frameworks and avenues for future scientific projects is supplied.

Originality/value

This study represents the first semi-systematic literature review on the organizational adoption of 3DP. Thus, it not only offers a valuable evaluation guide for potential adopters but also determines a future research agenda.

• Decision-making
• Manufacturing technology
• Organizational change
• Additive manufacturing
• 3D printing
• Technology implementation

Ukobitz, D.V. (2021), "Organizational adoption of 3D printing technology: a semisystematic literature review", Journal of Manufacturing Technology Management , Vol. 32 No. 9, pp. 48-74. https://doi.org/10.1108/JMTM-03-2020-0087

Emerald Publishing Limited

Copyright © 2020 2020, Desiree Valeria Ukobitz

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode 1 Department of Psychology, University of Konstanz, Konstanz, Germany 2 Department of Psychology, New York University, New York, USA 3 Institute of Psychology, Leuphana University Lüneburg, Lüneburg, Germany

1. Introduction

What is the current body of research regarding organizational adoption of 3DP in terms of descriptive indicators?

Which adoption theory and underlying decision criteria are most frequently applied for explaining organizational adoption of 3DP and what do we learn from these studies?

Where do we still lack knowledge, and therefore, which future research opportunities can be identified?

Existing literature reviews merely analyze publications on 3DP in terms of their frequency and count across different disciplines ( Gupta and Dhawan 2018 ). While the global research output in the field of 3DP amounted to 7300 papers in 2016, work examining 3DP from a management-science perspective is far more limited. Gupta and Dhawan (2018) in their assessment of 3DP research output encountered 372 publications on 3DP from the fields of business, accounting and management (2007–2016). Unfortunately, this study does not examine the underlying content in depth and as such does not generate learnings for theory and practice. Until to date, management science has not yet contemplated any review of literature on the organizational adoption of 3DP, i.e. the decision-making processes leading to 3DP adoption in firms. Knowledge on the adoption decision is yet fragmented, and unified sources of information to support the organizational decision-making process are scarce. As this area of investigation matures, the consolidation of existing knowledge on the underlying phenomenon is required more than ever. In an effort to fill this gap, this study aimed to examine the existing body of research on organizational 3DP adoption by means of a bibliometric analysis of predefined performance indicators, as well as a critical review of the theoretic constructs was employed to understand the phenomenon. Untapped opportunities of research are defined, and pathways for future studies are determined.

Until to date, the empiric evidence required to conduct a comprehensive literature review on the organizational adoption of 3DP was insufficient. However, during the last five years initial research exploring 3DP adoption through qualitative and quantities methods (e.g. Oettmeier and Hoffman, 2017 ; Schniederjans, 2017 ) evolved and granted first results on the distinct criteria affecting intrafirm adoption. Although the majority of qualitative studies employed explorative approaches to generate knowledge on this new field of research, recent work applied quantitative methods based on large empiric samples. These empiric analyses either applied firm-centered adoption models such as the technology–environment–organization (TOE) framework by Tornatzky and Fleischer (1990) (e.g. Yeh and Chen, 2018a ), the diffusion of innovation (DOI) theory by Rogers (1983) (e.g. Marak et al. , 2019 ) or user-centered decision frameworks, such as the unified theory of acceptance and usage of technology (UTAUT) by Venkatesh et al. (2003) (e.g. Schniederjans and Yalcin, 2018 ) or the technology acceptance model (TAM) by Davis (1985) (e.g. Chaudhuri et al. , 2018 ) to examine organizational decision-making. These models contrast not only in terms of the dependent- and independent variables but also in the underlying units of analysis. While some constructs study the action of adoption ( Tsai and Yeh, 2019 ), others merely examine the intention to adopt ( Oettmeier and Hofmann, 2017 ). Besides this characteristic, the dimensions impacting the adoption decision differ to a great extent. While the research study employing user-centered models emphasizes the impact of the individual decision-maker characteristics on the adoption decision ( Steenhuis and Pretorius, 2016 ), firm-centered models highlight the influence of external stakeholders ( Tsai and Yeh, 2019 ). Hence, a holistic overview of adoption criteria and drivers among firms would require analyzing the phenomenon from multiple theoretic angles.

The contributions of this paper are threefold. First, the bibliometric analysis reveals interesting aspects of the existing body of research. The most prominent characteristics of contemporary literature are reflected along descriptive indicators, such as industry, method, model, origin, research outlet or adoption drivers, thus granting relevant insights into academia and practice. Second, the most notable adoption models are carefully analyzed on their inherent attributes and their application fit for the context of organizational 3DP adoption. Findings, for instance, revealed the dominance of DOI across the existing body of research and divulge that this construct is generally applied in combination with user-centered decision frameworks to yield more precise results. Third, an ample range of opportunities for future research are detected and thoroughly explained. Among others, we identified a clear lack of information on the impact environmental variables and contingency factors exerted on the organizational adoption of 3DP. Guidance in relation to the sourcing of industry data, usage of adoption frameworks and avenues for future scientific projects is supplied.

The rest of this review is structured as follows. Section 2 provides an overview of the theoretical background underlying this investigation. The state of the art of 3DP is introduced, and the most commonly applied theoretic models for the adoption of technology are presented. Section 3 discloses the research methodology and explains the data collection and analysis process. Then, the descriptive results are presented and both quantitative findings from the reviewed publications, as well as the results from the latent content analysis, are demonstrated. Section 4 and 5 synthesize the results and discuss future research pathways. Finally, implications and limitations are presented.

2. Theoretical background

2.1 organizational vs consumer 3dp.

3DP has proliferated in the last 15 years among society and has gained vast attention, both, on an individual as well as on an organizational level. 3DP adoption among consumers ( Steenhuis and Pretorius, 2016 ), either as a facilitator to home fabrication ( Anastasiadou and Vettese, 2019 ) or as entrepreneurial starter kit ( Gartner et al. , 2015 ), has found increased awareness among research. Extensive media coverage and decreasing printer costs amplified the diffusion of 3DP on a consumer level. Hence, decision drivers and benefits of 3DP for individual consumers are widely studied ( Fox, 2014 ). The organizational adoption of 3DP, on the other hand, displays a rather untapped area of investigation. 3DPsystems and prices vary drastically depending on their purpose of usage, i.e. desktop or industrial application. Thus, decision-making for the adoption of 3DP on a firm level represents a complex process that involves the allocation of elevated financial and human resources, as well as organizational risks. A much broader range of decision drivers need to be considered for technology adoption on a firm level than on a user level ( Schniederjans, 2017 ). Turbulent market dynamics and firm-internal structures constitute only some of the complexities that need to be contemplated for the organizational adoption of novel technologies ( Tornatzky and Fleischer, 1990 ). Further investigation on this evolving field of research is required to support the managerial decision-making process and as such promotes the diffusion of 3DP among industry.

2.2 Understanding the technology adoption process

Multiple theoretical frameworks have been developed over time to study and understand the phenomenon of technology adoption from an empirical point of view. Depending on the unit upon analysis, these theories either observe adoption from an organizational or an individual perspective. To understand the phenomenon of organizational 3DPT adoption, this paper studies six theories of technology adoption. These frameworks have been selected based on their abundance as well as acceptance among technology adoption research. Among the most commonly discussed models are the theory of planned behavior (TPB) ( Ajzen, 1985 ), the TAM ( Davis, 1985 ), the UTAUT ( Venkatesh et al. , 2003 ), the TEO model ( Tornatzky and Fleischer, 1990 ), the DOI theory ( Rogers, 1983 ) and the institutional theory (IT) ( DiMaggio and Powell, 1983 ; Scott, 1995 ). Table 1 provides an overview of these adoption theories, their aim, drivers, dependent variable, applicability for organizational adoption, applicability among 3DP research as well as references. Dependent variables vary from intention to adopt to adoption behavior, thus emphasizing either actual or potential 3DP adoption.

2.2.1 Theory of planned behavior

The TPB aims to explain and predict human behavior , i.e. action. TPB assumes a behavioral intention prior to the actual behavior and hypothesizes that this intention is influenced by the individual’s attitude toward the behavior , the individual’s subjectively perceived norm of what should be done ( subjective norm ) and the individual’s degree of control over the factors influencing the behavior ( perceived behavioral control ) ( Ajzen, 1985 ). Although TPB was not designed to study technology-related decisions, the theory still sets the basis for major studies on human behavior and as such contributes to extended research on technology adoption among individual members of society. TPB explains the intention to adopt rather than the actual adoption behavior; thus, the time frame between intention and behavior is unknown. Both Schniederjans and Jalcin (2018) as well as Chatzoglou and Michailidou (2019) in their research on the organizational adoption of 3DP have included elements of TPB, such as to understand management’s attitude toward using 3DP. Both scientists studied the managerial intention to adopt 3DP by means of the managers’ attitude toward and perception of the technology. A major limitation of TPB is the lack of account for environmental or economical dimensions of influence on the intention to adopt 3DP. As such, TPB merely represents a tool to predict managerial behavior prior to actual adoption. Research on the organizational adoption of 3DPT has employed TPB only in combination with other adoption models, contemplating additionally for technological, organizational or environmental dimensions (e.g. DOI, TAM and UTAUT).

2.2.2 Technology acceptance model

The TAM aims to predict how users accept and employ technology and draws on behavioral aspects of TPB. TAM hypothesizes that technology usage is influenced by the attitude toward technology usage and consequently the intention to use the technology . The model claims that an individual’s attitude toward technology usage is derived from the user’s perceived ease of use (PEOU) and perceived usefulness (PU) of the technology, and it further acknowledges that external variables influence and moderate the relationship between PU and PEOU ( Davis, 1985 ). The body of research on technology adoption often criticizes the limited predictability of TAM and thus expanded the model to TAM II, including the variables of social influence and cognitive instrumental processes ( Venkatesh and Davis, 2000 ). While the users’ perceived ease of use as well as the perceived usefulness of 3DP represent helpful dimensions in understanding acceptance or rejection of 3DP, they do not explain actual adoption. Commonly labeled as intention-behavior gap, intention is an insufficient prerequisite for a successful action ( Sheeran and Webb, 2016 ). Similar to TPB, TAM I and II represent models that aim to analyze the adoption intention of individual members of society as opposed to organizations. TAM does not take environmental and organizational drivers, such as market dynamics or human resources, into consideration. As such, contemporary research on organizational 3DP adoption has employed TAM mostly in combination with the DOI theory ( Rogers, 1983 ), such as to constitute for the lack of context ( Wang et al. , 2016 ; Oettmeier and Hofmann, 2017 ; Marak et al. , 2019 ). Contemporary research often employs TAM to better comprehend the 3DP adoption intentions among top-management members ( Schniederjans and Yalcin, 2018 ). So far only TAM I has been studied by the underlying body of literature on organizational 3DP adoption.

2.2.3 Unified theory of acceptance and usage of technology

The unified theory of acceptance is based on various aspects of the aforementioned models and aims to explain the behavior of people in their use of technology. UTAUT is acknowledged as one of the most complete frameworks for predicting adoption behavior among individual members of society. UTAUT proposes that usage intention and facilitating conditions directly influence technology usage . Furthermore, the model suggests that the technology usage intention is directly determined by three key constructs ( performance expectancy, effort expectancy and social influence ). Additionally, four moderator variables ( gender, age, experience and voluntariness of use ) impact the relationship between the different exogenous and endogenous variables ( Venkatesh et al. , 2003 ). The increased amount of moderator variables is frequently criticized as artificially improving UTAUTs predictability ( Dwivedi et al. , 2019 ). Similar to TAM, UTAUT is also suffering from the intention-behavior gap, thereby examining intention to use a technology rather than its actual adoption. Alike TPB and TAM, UTAUT may also be applied to study the individual behavior of managers in organizations; however, it does not consider the impact of technology adoption on the organization from a holistic point of view. Despite the inclusion of social influence (e.g. society) and facilitating conditions (e.g. resources) as independent variables defining an individual's intention to adopt, organizational dynamics (e.g. competitors and human resources) and external dimensions (e.g. market and environment) are not contemplated. Hence, to reliably examine the phenomenon of organizational 3DPT adoption, research combined UTAUT with the DOI theory ( Rogers, 1983 ) ( Schniederjans, 2017 ; Marak et al. , 2019 ).

2.2.4 Diffusion of innovation theory

The DOI theory analyzes how, why and at what rate new ideas or technology diffuse among a social system over time ( Rogers, 1983 ). DOI contributes the adoption decision to innovation-specific criteria and suggests that decision-makers undergo a thorough evaluation of the technology´s characteristics, both firm internal as well as external. As such, DOI theorizes that a technology’s relative advantage for the adopting organization, its compatibility with existing technological structures, its perceived complexity , the observability of technology-induced success as well as its anteceding triability all impact the adoption decision ( Rogers, 1983 ). As opposed to TPB, TAM and UTAUT, DOI acknowledges the context upon which technology adoption-decisions are taken and as such constitutes a proper theory for analyzing organizational technology adoption. DOI represents the most frequently applied method for examining the adoption of 3DP in organizations ( Oettmeier and Hofmann, 2017 ; Schniederjans, 2017 ; Chaudhuri et al. , 2018 ; Marak et al. , 2019 ). As DOI aims to understand the DOI among a social system over time, the theory focuses on the bigger picture of adoption rather than emphasizing on multiple distinct firm-internal and external drivers. Scientists employ this method to obtain a broad overview of the determinants of 3DP adoption in firms ( Chatzoglou and Michailidou, 2019 ). While the theory already comprehends a vast range of drivers for organizational technology adoption, still insufficient emphasis is put on the impact of environmental factors ( Hsu et al. , 2006 ), such as stakeholders influence, industry infrastructure or governmental regulation. Especially when examining the adoption of 3DP, environmental dimensions constitute major decision drivers (e.g. governmental funding/subsidies and commercial partner infrastructure). Moreover, research on 3DP adoption frequently combines DOI with individual adoption models (TAM and UTAUT), such as to dive deeper into understanding of individual decision-maker’s motivations ( Wang et al. , 2016 ). All in all, DOI is well-suited for generating an overview on the drivers for 3DP adoption in firms; however, if more profound insights are required, further specific dimensions need to be added to the model (e.g. environmental, organizational and individual) to obtain profound insights.

2.2.5 Technology–organization–environment framework

The TOE framework identifies three crucial aspects that influence technology adoption within an organization. TEO argues that technology- organizational- and environmental-related factors drive adoption. Technological factors describe the perceived characteristics of the technology in terms of its relative advantage for the organization and compatibility with existing structures. Organizational-related motives refer to internal characteristics such as firm size, financial and human resources, internal structure and future vision as well as outlook. Ultimately, environmental factors define all firm-external drivers such as industry dynamics, competitors, trading partners and authorities ( Tornatzky and Fleischer, 1990 ). As opposed to the aforementioned models, TEO examines the technology adoption decision rather than the mere intent of adoption. TEO is frequently employed to examine the adoption of radical technology and also found initial application in the literature on the adoption of 3DP (e.g. Yeh and Chen, 2018a ). The framework is consistent with DOI in terms of its general drivers, however complements the model through its emphasis on the environmental context ( Wang et al. , 2016 ). TEO, on the contrary to DOI, delves deep into the impact of industry stakeholders, governmental entities, market trends or legislation on the organizational adoption decision. Although TEO does not specify decision-maker characteristics to the extent of DOI, TPB, TAM or UTAUT, the management’s experience, vision and support receive attention in the variables constituting the organizational context. The literature on the organizational adoption of 3DP has employed TEO on its own. While no combinations with other adoption models have yet been conducted in this area of research, Tsai and Yeh (2019) and Yeh and Chen (2018) have added the independent variable of 3DP cost to the construct. As TOE has been developed specifically to examine complex technology adoption decisions from an organizational perspective, it emphasizes all processes and variables that impact the adopting entity as part of an industrial ecosystem. As such, extended research on technology adoption manifested that TEO is more appropriate to analyze intrafirm adoption than DOI ( Hsu et al. , 2006 ).

2.2.6 Institutional theory

IT argues that firm-external pressures lead to organizational actions and behavior ( DiMaggio and Powell, 1983 ). The scientific field of innovation and technology management frequently draws back to sociology and as such to IT to observe the phenomenon of isomorphism in decision-making (e.g. Teo et al. , 2003 ). IT hypothesizes that coercive, normative, and mimetic isomorphic pressures exerted by an organization's environment influence firm’s internal decisions. While coercive forces result from trading partner behavior, normative pressure is exerted from industry authorities and mimesis arises from competitor actions ( DiMaggio and Powell, 1983 ; Scott, 1995 ). As such, IT acknowledges that organizational decisions are not only driven by performance goals (e.g. relative advantage) but also by social and cultural factors. To emphasize the influence of stakeholder dynamics on adoption decisions, research on radical technology, frequently applies IT in combination with other adoption models (e.g. Yoon and George, 2013 ; Cao et al. , 2014 ). In order to highlight the impact of institutional pressures exerted on the adopting entity (e.g. from trading partners, competitors and institutions), the literature oftentimes proposes integrating IT in the context of environment as proposed by TEO ( Soares Aguiar and Palma-dos-Reos, 2008 ; Oliveira and Martins, 2011 ). Until to date, only one investigation examined the organizational adoption of 3DP by means of IT ( Schniederjans and Yalcin, 2018 ). Although IT constituted one of many models that have been employed by Schniederjans and Yalcin´s (2018) study, compelling evidence was found for the impact of isomorphic pressures on the adoption of 3DP in organizations. Unfortunately, no quantitative research has yet analyzed the 3DP adoption from an institutional lens. IT embodies a powerful approach to delve into the frequently overlooked impact of institutional forces on technology adoption decisions.

3. Research methodology

What is the current body of research regarding organizational adoption of 3DP in terms of descriptive indicators (methods and models applied, industries investigated, research activity along country and time, research outlet and frequency of drivers)?

Which adoption theory and underlying decision criteria are most frequently applied for explaining organizational adoption of 3DP, and what do we learn from these studies?

Where do we still lack knowledge, and therefore which future research opportunities can be identified in the field of organizational adoption of 3DP.

3.1 Description of analysis process

Once the purpose of the present research was established, the literature selection process was initiated. Screening for inclusion criteria was developed to identify the most appropriate literature for analysis ( Fink, 2014 ). The analysis commenced with an extensive keyword search to source relevant literature studies on the adoption of 3DP technologies. The databases employed were Business Source Complete, Science Direct and Web of Science. The predefined criteria were applied to reduce the search to the most relevant publications in the field of organizational 3DP adoption. Afterward, the articles were quantitatively analyzed on their year of publication, publication outlet, authorship and origin, underlying research methods and applied theoretical frameworks as well as industries upon investigation. This was followed by the content analysis of the underlying drivers for 3DP adoption. The information was coded and synthesized to a higher level of dimensions based on the TOE framework ( Tornatzky and Fleischer, 1990 ).

3.2 Search and selection process

The focus of this paper lies on the understanding of the organizational decision-making process for the adoption of 3DP. Thus, the following keyword string was generated to conduct the search across the three aforementioned databases: (“ 3D printing ” OR “ additive manufacturing ” ) AND (“ adoption ” OR “ decision ” OR “ usage ” OR “ application ” ) . While it was set as a prerequisite for the selected titles to include either the term 3DP or additive manufacturing , the keywords invariably had to include the label adoption OR decision OR usage OR application . The keyword diffusion was not included on purpose, as it represents the macro phenomenon of how many entities of the population have already adopted the technology over time. The search identified a total of 594 fully available articles.

To assure high quality and applicability of the research articles prespecified inclusion criteria were applied to the literature search ( Fink, 2014 ). First, the years of publication were restricted from 2010 to March 2020, as hardly any empiric research on the adoption of 3DP resulted before that year. The number of articles reduced to 483. Second, the search was narrowed down to peer-reviewed articles such as to guarantee scientific rigor of articles (401 articles). Next, we limited the search to English-only publications, thereby reducing the body of literature to 392. Fourth, we reduced the selection to articles originating from management journals only, excluding chemistry, biomedical and in-depth engineering outlets as these hardly draw attention to organizational decision-making processes. In total, 85 articles matched all the criteria and were selected for further content analysis. In the fifth step, we scanned the abstracts of all obtained articles on their fit for the underlying research. Research studies that did not strictly emphasize the organizational process of adopting 3DP and associated decision drivers were excluded. First, we omitted articles discussing the adoption of 3DP among individuals rather than organizations (49). Second, while articles based on quantitative research methods had to apply at least one adoption model, papers based on qualitative analysis had to discuss either drivers, barriers or other factors impacting the organizational adoption decision. As such, and for example, articles analyzing the impact of 3DP on supply chain and inventory or articles examining the adoption, advantages and disadvantages of different 3DP techniques in organizations were removed. In conclusion, 25 publications were identified and selected for this review. We conducted a backward search of all 53 articles to study the referenced literature for any further research ( Levy and Ellis, 2006 ). A total of two further studies were detected, summing up to a final total of 27 papers. In total, two additional research papers that would not have passed the predefined screening criteria have been identified by informal sources and included due to the fact that their results were of interest for the study, making a total of 29 articles. Next, the final selection of papers was evaluated on their quality ( Fink, 2014 ). Qualitative and quantitative research was treated differently. While quantitative research was assessed on the underlying data collection methodology as well as reliability and validity of results, qualitative research was evaluated on the explicitness, comprehensiveness and reproducibility of the employed empirical methodology. No papers were found to lack reliability or empiric evidence. Figure 1 graphically illustrates the selection process and reveals corresponding data.

3.3 Data extraction and analysis

To conduct the bibliometric literature review as well as the underlying content analysis, data needed to be extracted in a systematic manner. Qualitative and quantitative research had to be treated differently ( Rousseau et al. , 2008 ). To further proceed with the analysis, a spreadsheet database was generated. The bibliometric results of the selected articles were thoroughly examined, and the following information was retrieved: article title, authors, location and affiliation, journal, date of publication, keywords, research type, research method, theoretical framework and industry. Microsoft Excel was employed to synthesize and visually represent the recaptured data. Next, data were extracted for the content analysis. In this step, the adoption criteria were obtained from quantitative and qualitative studies. In terms of quantitative research, only those criteria that tested significant on the adoption decision were selected. Qualitative studies were subjected to individual researcher’s judgment; thus, the most frequently cited and evidenced decision criteria were extracted. For the sake of analysis, the selected articles were systematically analyzed, and data were coded according to predefined schemes. The TEO framework was employed to schematize the criteria underlying the organizational adoption of 3DP. Retrieved data were categorized following a deductive category approach and organized in terms of technology-, organization- and environment-specific drivers. The coding process was carried out in MAXQDA v.12. Finally, the results were quantitatively analyzed on their frequencies.

4. Descriptive results

4.1 trend among publications in time.

The literature review confirmed the novelty of the field of 3DP adoption for academia. As shown in Figure 2 , research activity was relatively thin before 2015. The topic first received attention in 2013, however hardly continued in the scientific radar for the next three years. The data observe that the adoption of 3DP from a business and innovation science perspective gained increased interest from 2015 onwards and peaked in 2018. This trend resonates with the overall rate of the technology’s diffusion among industry. Before 2010, 3DP was used mainly by the high-tech sector for rapid prototyping or concept proof. As a result of increased media exposure, the emergence of sophisticated 3DP suppliers as well as decreasing printer costs, the technology has heavily started to enter more mainstream industry after 2010 ( Sculpteo, 2019 ). Thus, empiric data on adoption behavior and trends became available only some years after.

4.2 Publication outlet

Figure 3 illustrates the most frequently employed journals in the field of 3DP adoption. Most articles have been published in Journal of Manufacturing Technology Management (5). An equal number of papers were found in Technology Forecasting and Social Change, International Journal for Production Economics and International Journal for Production Research (3). In total, two publications brought to our awareness by informal sources have been obtained through university databases (doctoral and master thesis). The remaining publications pertained to highly specific journals in the field of innovation management or manufacturing. These findings agree with Bradford’s law observing that a core of journals produces approximately a third of all articles ( Eyers and Potter, 2015 ).

4.3 Authorship and location

In total, 71 authors contributed to the identified literature on 3DP adoption. Approximately 45% of the identified body of research was elaborated by two authors and an additional 21% by three authors. Single-authored articles contributed only to 17% of the grand total, followed by contributions from four and five researchers. Only four out of 29 papers were developed through crosscountry collaborations and mostly constituted interEuropean research alliances. The primary authors country of residence was a selected as main location. The adoption of 3DP was analyzed by academics across various countries; however, it flourished in the USA and Europe.

4.4 Research methods and theoretical framing

The selected articles were furthermore examined on their underlying research methods. Figure 4 illustrates the distribution of publications based on the methodology employed as well as the year published. As visible, the early years of investigation were characterized by explorative research, conducted either through desk research, semistructured interviews or case studies. As a result of the topic's novelty and the consequent lack of empirical evidence, the most commonly employed research method is the in-depth interview with a total number of 12 publications. Once 3DP adoption diffused among industry, the first quantitative data became available and allowed survey-based research methods to consolidate in 2019. Moreover, the first empirical results derived through adoption theories emerged in 2016 along with quantitative studies. Desk research appeared consequently throughout the last ten years, indicating a consistent diffusion of 3DP across organizations.

Figure 5 illustrates the adoption models that have been employed to analyze the organizational adoption of 3DP along the different research methods. The DOI framework was the most employed theory among the underlying body of research, tightly followed by the technology acceptance model. The UTAUT was resulted as the third most-frequently applied model among publications. The TEO model as well as the TPB were both employed by an equal number of publications. Only one paper considered IT for analyzing 3DP adoption. While 76.5% of all quantitative research papers were based on their analysis on specific adoption models, only 47.7% of qualitative papers did so. Half of the publications represent explorative approaches that did not employ adoption theories. In total, eight publications combined indicators and aspects from multiple models. While six papers combined the TAM and the DOI theory, three articles merged DOI and UTAUT. Thus, DOI, UTAUT and TAM represent the most combined concepts among the underlying body of research.

4.5 Industries upon analysis

All reviewed publications based on qualitative and quantitative research methods focused their empirical analyses either on one or multiple industries ( Figure 6 ). Only four articles emphasized their investigation on a single industry, and one-fourth of these articles applied a qualitative research approach. The remaining publications under review, especially all quantitative research papers, revealed data corresponding to multiple industries. This may be the result of the novelty of the topic and still the limited amount of information on specific industries. In total, 15 articles investigated the organizational adoption of 3DP in an industrial manufacturing setting. These publications however did not state any further sub-categorization. The transport industry (automotive and aerospace) was analyzed by one-third of all articles, closely followed by the consumer good industry (textile, jewelry, furniture and sports equipment) and the health and medical sector. Additional settings upon analysis were represented by the electronic, chemical and construction industry. One study can analyze several industries.

4.6 3DP adoption criteria

This section provides a categorization of the criteria influencing the adoption of 3DP and highlights the most interesting learnings associated with these decision drivers. While an ample range of theories have been applied to study 3DP adoption (see Figure 5 ), the underlying research aims to display the most recurrent adoption criteria along the TEO framework, as it represents one of the most appropriate models for analyzing intrafirm technology adoption ( Hsu et al. , 2006 ). As described in section 3.3 , the underlying extraction process differed among qualitative and quantitative studies. Out of the quantitative studies, only those criteria that proved significant in the empirical analysis were identified and counted for the underlying research. In the qualitative studies, those criteria were selected/counted because the authors of the studies found those to be the most influencing ones. As suggested by Weber (1990) criteria were coded as words. Following a deductive approach ( Neuendorf, 2017 ), the criteria were extracted and schematized along three dimensions. The technology context includes all factors that relate to the benefits and barriers, and the technology per se represents for the organization in terms of performance, impact and agility. The organizational context defines all company-internal aspects that impact the adoption decision, both positively as well as negatively. As such, these criteria refer to structural requirements (i.e. size, budget and processes) as well as to organizational readiness (i.e. top management support, technology readiness and experience). Finally, the environmental context describes all firm-external factors that impact the adoption decision. Validity is established through data triangulation, as different sources of data have been reviewed to develop the analysis ( Neuendorf, 2017 ). Table 2 illustrates the critical factors for adoption along three categories and indicates their frequency among the publications selected for this literature review.

4.6.1 Technology context

As observed in Table 2 , technology context represents the most prominent category of adoption drivers with a frequency of 167. Investment costs ( f  = 16) represent the most dominant barriers to adoption in terms of frequency. Literature points out high technology acquisition costs, unexpected maintenance costs as well as increased material pricing as important aspects of 3DP adoption (e.g. Yeh and Chen, 2018 ). This is followed by concerns on the technological maturity of 3DP ( f  = 13). In this context, the scientific community commonly observes the lack of standardization among printers and output quality (e.g. Weller et al. , 2015 ). In total, six articles emphasized the slowness of the production process that seems rather uncompetitive when compared to traditional manufacturing methods (e.g. Fontana et al. , 2019 ). The review reveals that the technology-specific benefits still outweigh the barriers in terms of frequency among articles. The majority of papers emphasize the possibility to accelerate time to market ( f =  16) through 3DP and elaborates on its positive impact on lead- and ramp-up times as well as manufacturing cycles (e.g. Schniederjans, 2017 ). The opportunity to simplify supply chains ( f =  11), thereby lowering inventory and production steps, as well as skipping tooling and molding functions, represents a further highly quoted driver for 3DP adoption (e.g. Oettmeier and Hofmann, 2017 ). Among the most prominent criteria for 3DP, adoption is the ability to customize products for end users ( f =  9) (e.g, Murmura and Bravi, 2018 ). Cohen (2014) states that 3DP allows the mass customization of up to 200 products a time. The reduction of the environmental impact ( f =  9) through the additive manufacturing technique (zero waste), the possibility to experience absolute design freedom ( f =  9) and create complex products or the option to manufacture small production batches ( f =  4), among others, represent further benefits driving 3DP adoption (e.g. Marak et al. , 2019 ). Interestingly, the limitation in terms of size of printed products and printers per se was only pointed out twice (e.g. Weller et al. , 2015 ). While the organizational context observes the importance of skilled human resources, only one publication mentioned software usage (CAD) as a barrier to 3DP adoption in organizations ( Garza, 2016 ).

4.6.2 Organizational context

Organizational drivers represent the second most important category for adoption among the reviewed literature ( f =  56). The most frequently quoted aspect for 3DP adoption among investigated cases is the organizational readiness ( f =  12). Among others, organizational readiness refers to the firms' willingness to adopt 3DP, its experience with similar technology and the degree of internal rejection (e.g. Candi and Beltagui, 2019 ). The existence of skilled workforce and the concomitant necessity of reskilling existing workforce ( f =  11) exhibit the second most mentioned factor impacting 3DP adoption from an organizational perspective (e.g. Chaudhuri et al. , 2018 ). Furthermore, it seems of utmost importance to evaluate the technologies compatibility ( f =  9) with existing production systems as well as the overall fit with the company’s overall mission and structure (e.g. Tsai and Yeh, 2019 ). The literature also repeatedly acknowledges the support and experience of top management teams ( f =  7) as well as the importance of a dynamic organizational culture ( f =  5 ) for3DP adoption (e.g. Mellor et al. , 2014 ). Some articles further emphasized the importance of the alignment among firm-internal departments ( f =  4), such as manufacturing and IT for 3DP adoption. An increased company size was found to be both, a promoter as well as an inhibitor of organizational 3DP adoption (e.g. Kianian et al. , 2016 ). Steenhuis et al. (2020) manifested the impact of company age and location on the adoption behavior.

4.6.3 Environmental criteria

Although, highly significant, environmental decision criteria appear less frequently among the 3DP literature and only represent a total of 31 quotes in this research. This results from the limited application of adoption models emphasizing firm-external decision drivers such as TEO or IT, among the underlying body of literature. Oettmeier and Hoffman (2017) emphasize the impact of coercive forces ( f =  5) exerted by trading partners, competitors ( f =  3) as well as the overall effect of social influence ( f =  5) on the adoption decision. Existing research repeatedly acknowledges market and technology turbulence ( f =  4 ) as well as the overall competitiveness of the industrial environment as adoption triggers (e.g. Candi and Beltagui, 2019 ). Various papers referred to facilitating conditions ( f =  5) (governmental or regulatory support and training initiatives) as incentive for 3DP adoption (e.g. Oettmeier and Hofmann, 2017 ). The readiness of the 3DP supplier landscape, in terms of number of technology and material vendors ( f =  4), also appears to play an important role for organizational technology adoption ( Tsai and Yeh, 2019 ). Furthermore, compliance with evolving market trends and expectations ( f =  2) was found as a driver to adoption ( Yeh and Chen, 2018a ).

5. Discussion of results

Although the organizational adoption of 3DP has gained increased scientific interest from 2015 onwards, our findings show that this field is still in its infancy. This understanding resonates with the overall diffusion of 3DP technology among industry ( Wohlers Associates, 2019 ). Until to date, research has been dominated by qualitative methods, such as to generate a common understanding of the topic and yield first explorative results on the factors driving 3DP adoption in organizations. Quantitative research activity was commenced in 2016, along with the availability of industry data, and since then it is employed in multiple adoption models to study the phenomenon in an empiric manner.

5.1 Theoretic constructs

While the most commonly applied adoption theory across the underlying body of research was the DOI, almost all studies combined this model with at least with one additional construct (TPB, UTAUT or TAM. TPB, UTAUT and TAM are user-centered decision models that emphasize the perception and characteristics of an individual toward an action, i.e. technology adoption. We discovered that, besides analyzing the impact of the technology per se (e.g. relative advantage) through DOI, this specific combination stresses the influence of top management on the adoption decision. The dimensions driving technology adoption among firms however differ vastly from user-centered adoption processes. Thus, they require a more holistic approach to analyze not only the technology and the individual decision-maker but also the organizational and environmental characteristics a firm is immersed into. The preceeding theoretic constructs, however, hardly study the adoption decision from an institutional angle. The TOE model and the IT on the other hand contribute organizational decision-making to environmental factors of influence. Even though the body of literature on the organizational adoption of radical technologies frequently emphasizes the impact of firm-external conditions on the adoption decision ( Wang et al. , 2010 ; Cao et al. , 2014 ), this topic has hardly experienced any discussion in the field of 3DP.

5.2 Data characteristics and origin

Our analysis suggests that as opposed to DOI, TPB, UTAUT and TAM, TEO and IT require industry-specific data to yield most adequate results. Industry homogeneity across the sample is a prerequisite to obtain conclusive and generalizable results on the impact of industry dynamics on technology decision-making. Due to the novelty of the field, industry data are still premature. Most quantitative studies have acquired their data from organizations pertaining to multiple industries. Only four out of 29 papers have employed a single-industry approach. Such heterogeneous results neither allow to identify differences in organizational behavior across industries nor to propose industry-specific adoption plans. The limited rate of diffusion across industries ( Steenhuis et al. , 2020 ) and the associated lack of information represent a major impediment for investigating the phenomenon from an holistic angle. We conclude that corporate adoption decisions ultimately have to be examined from an environmental, organizational, and technological point of view. We propose TEO as the most complete option for analyzing the phenomenon of 3DP adoption in organizations. Besides the integration of industry specific variables, TEO also represents the only model that analyzes the action of adopting a technology rather than the intent to adopt. This again is a reflection of existent industry data and sample characteristics. While research based on DOI, TPB, UTAUT and TAM included 3DP adopters and nonadopters in their sampling process, TEO emphasizes only those cases that have already taken a decision. Studying adoption, as opposed to hypothetical adoption, allows to draw conclusions on the actual impact of firm-internal and external drivers on the adoption decision.

5.3 Adoption drivers

Content analysis furthermore revealed that the technological dimensions, as compared to the organizational or environmental dimensions, of influence have received most attention among the scientific community. More than half of all researched articles recognized the significance of technology-related adoption drivers, such as the ability of 3DP in accelerating time to market, its ease of use and the associated simplification of supply chains. Furthermore, a third of all articles acknowledged the firm’s overall innovativeness, the existence of skilled work and characteristics of top-management as critical decision drivers. Contrarily, hardly any discussion occurred on the influence of organizational contingency factors (e.g. firm size and firm age) on adoption. Additionally, overall information on the impact of market trends, facilitating conditions, institutional pressures or supplier landscape is limited. While some articles discuss the effect of external pressures on the adopting entity ( Oettmeier and Hofmann, 2017 ; Schniederjans, 2017 ; Tsai and Yeh, 2019 ), no differentiation is made among its source of emission. According to IT, pressure is exerted from competitors, trading partners and authorities. The impact of isomorphism on the adoption of radical manufacturing technology is well known in the contemporary literature (e.g. Kuan and Chau, 2001 ; Alshamaila et al. , 2013 ), however understudied in the field of 3DP.

We conclude that the lack of industry data is limiting both, the extent of quantitative research methods across the field of organizational 3DP adoption as well as the application of integral theoretical constructs. This results in a polarized set of findings, with vast knowledge being generated on the benefits and barriers of the technology for the organization, but relatively little information on the impact of a firm’s environment or contingency factors exert on the adoption decision was obtained. Findings of interest for the managerial audience are yet limited, as hardly any in-depth information on specific industries was generated so far, neither through survey nor case studies. Moreover, most existent results emphasized the intent of adoption rather than the experience of already adopters.

6. Opportunities for future research

The underlying work detected various untapped areas of research; opportunities for future investigation are highlighted in the following paragraphs.

6.1 Maturity of industry data

In-depth analysis of the organizational adoption of 3DP and underlying drivers requires more and specialized industry data, both from (1) specific industries as well as from (2) 3DP adopters. First, future research should emphasize on the action of adoption rather than the intent, and thus investigate adoption behavior among organizations that have already integrated 3DP into their manufacturing processes. This would not only support in validating past hypothesis but also induce more generalizable results on 3DP adoption. Second, as adoption behavior varies across industries, data from single industries, either quantitatively or qualitatively (industry case study) derived, would allow to analyze the impact of the industrial environment on the adoption decision. Results from single industries would constitute a useful tool for comparing 3DP diffusion and adoption characteristics across industries and moreover facilitate decision-making for future adopters.

6.2 Characteristics of adopters vs nonadopters

We recommend future studies to compare the characteristics of adopting and nonadopting organizations from an empirical perspective. The frequency analysis revealed that organizational readiness represents one of the most crucial drivers for adoption. A comparison among adopters and nonadopters would yield valuable insights into how to prepare firms for technology adoption. Also the experience of top management with the technology seems of utmost importance for successful organizational adoption. Here future research could investigate the impact of top management experience on 3DP usage by means of user-centered models (UTAUT and TAM).

6.3 Emphasis on the environmental context

The underlying research has placed little emphasis on the impact of firm-external factors on the organizational adoption decision. The environmental context represents only 12% of overall quotes in our content analysis. Hardly any research studied the impact that stakeholders have on organizational adoption behavior. Governmental and regulatory support, i.e. subsidies or educational initiatives or restrictions, i.e. environmental legislation, IP rights, received even less attention among literature studies. As such, profound analyses of the impact of institutional drivers (industry stakeholders: competitors, trading partners and government) on the adoption decision through IT would represent a fruitful area of research. Future studies might also conduct semistructured interviews with regulatory institutions to understand how contemporary regulation and legislation promotes or inhibits the diffusion of 3DP among industries.

6.4 Impact of contingency factors

While the impact of organizational characteristics, such as technology readiness and existence of skilled workforce, was frequently validated among existing research, contingency criteria were hardly investigated. Firm origin, size and age may be of utmost importance for technology adoption. Future research could aim to answer the question of whether firm age impacts the technology adoption decision, thus whether incumbent firms are more likely to adopt 3DP technology than new entrants. Furthermore, a macro study may be conducted to holistically analyze the phenomenon, applying TEO and integrating contingency factors as independent rather than moderator variables.

6.5 Metaanalysis on adoption drivers

While the content analysis revealed that the relative advantage offered by 3DP represents a significant adoption driver across almost all quantitative studies, other drivers appear to depend on the organizational and environmental context the firm is immersed into. Until today, no metaanalysis has yet been conducted to study the relevance of single adoption criteria across the quantitative studies on the organizational adoption of 3DP. Future research by means of metaanalysis could support the existing body of research in generalizing and validating existent findings.

7. Implications and limitations

This study thoroughly reviewed the existing body of research on the adoption of 3DP and generated valuable insights into both academia as well as management. Our findings support this growing scientific community in encountering untapped areas of research and channeling upcoming scientific projects. The bibliometric analysis of literature conveys an overview of the existing body of research, patterns and opportunities. Additionally, the critical analysis of contemporary adoption models, as well as their inherent characteristics, allows to better understand the interplay between results and theory, thus supporting future research in selecting the most appropriate adoption model. As such, the underlying review represents a valuable base of information, theory and sources for any empirical work on 3DP. Our results also offer relevant insights for practice, especially managerial decision-makers on the verge of technology adoption. The adoption drivers revealed in our study represent appropriate factors of consideration for manufacturing managers during the decision-making process. Furthermore, our results generate awareness on the effect 3DP adoption exerts on the different areas of an organization, thus supporting management in appraising the impact of technology integration in a holistic manner. Hence, the underlying work can serve as a guide for decision-making and as support in evaluating all possible implications of 3DP adoption in organizational environment.

Even though an ample range of literature was examined to develop the underlying review, and the screening criteria employed were developed in an inclusive way, there may still exist literature of interest that has not been included. Furthermore, it is paramount to acknowledge that due the nature of content analysis, the prominence of the adoption criteria is measured in terms of frequencies as opposed to their effective impact on the phenomenon under analysis. Thus, content analysis cannot serve to generalize the impact of particular drivers on the adoption of 3DP. To measure the impact, the obtained results would require to be quantitatively tested.

Publication selection strategy and final selection

Distribution of publications from 2010 to 2020

Distribution of publications across journals

Number of publications per year and per research method

Theoretical perspectives used in publications

Industries upon analysis (multiple industries per publication)

Adoption theories

3DP adoption drivers in organizations

Note(s) : * Relevant authors but not exclusive

Ajzen , I. ( 1985 ), From Intentions to Actions: A Theory of Planned Behavior. Action and ControlFrom Cognition to Behavior , Springer-Verlag , New York .

Alshamaila , Y. , Papagiannidis , S. and Li , F. ( 2013 ), “ Cloud computing adoption by SMEs in the north east of England: a multi-perspective framework ”, Journal of Enterprise Information Management , Vol. 26 , pp. 250 - 275 , doi: 10.1108/17410391311325225 .

Anastasiadou , C. and Vettese , S. ( 2019 ), “ From souvenirs to 3D printed souvenirs. Exploring the capabilities of additive manufacturing technologies in (re)-framing tourist souvenirs ”, Tourism Management , Vol. 71 , pp. 428 - 442 .

Attaran , M. ( 2017 ), “ The rise of 3-D printing: the advantages of additive manufacturing over traditional manufacturing ”, Business Horizons , Kelley School of Business, Indiana University , Vol. 60 No. 5 , pp. 677 - 688 , doi: 10.1016/j.bushor.2017.05.011 .

Candi , M. and Beltagui , A. ( 2019 ), “ Effective use of 3D printing in the innovation process ”, Technovation , Vols 80–81 , November 2017 , pp. 63 - 73 , doi: 10.1016/j.technovation.2018.05.002 .

Cao , D. , Li , H. and Wang , G. ( 2014 ), “ Impacts of isomorphic pressures on BIM adoption in construction projects ”, Journal of Construction Engineering and Management , Vol. 140 No. 12 , pp. 1 - 9 , doi: 10.1061/(ASCE)CO.1943-7862.0000903 .

Chatzoglou , P. and Michailidou , V. ( 2019 ), “ A survey on the 3D printing technology readiness to use ”, International Journal of Production Research , Vol. 57 No. 8 , pp. 2585 - 2599 , doi: 10.1080/00207543.2019.1572934 .

Chaudhuri , A. , Rogers , H. , Soberg , P. and Pawar , K. ( 2018 ), “ The role of service providers in 3D printing adoption ”, Industrial Management and Data Systems , Vol. 119 No. 6 , pp. 1189 - 1205 , doi: 10.1108/IMDS-08-2018-0339 .

Cohen , D. ( 2014 ), “ Fostering mainstream adoption of industrial 3D printing: understanding the benefits and promoting organizational readiness ”, 3D Printing and Additive Manufacturing , Vol. 1 No. 2 , pp. 62 - 69 , doi: 10.1089/3dp.2014.0007 .

Davis , F. ( 1985 ), A Technology Acceptance Model for Empirically Testing New End-User Information Systems : Theory and Results , Massachusetts Institute of Technology , available at: http://hdl.handle.net/1721.1/15192 .

DiMaggio , P. and Powell , W. ( 1983 ), “ The iron cage revisited: institutional isomorphism and collective rationality in organizational fields ”, American Sociological Review , Vol. 48 , pp. 147 - 160 , available at: http://10.0.9.3/2095101 .

Dwivedi , Y. , Rana , N. , Jeyaraj , A. , Clement , M. and Williams , M. ( 2019 ), “ Re-examining the unified theory of acceptance and use of technology (UTAUT): towards a revised theoretical model ”, Information Systems Frontiers , Information Systems Frontiers , Vol. 21 No. 3 , pp. 719 - 734 , doi: 10.1007/s10796-017-9774-y .

Eyers , D.R. and Potter , A.T. ( 2015 ), “ E-commerce channels for additive manufacturing: an exploratory study ”, Journal of Manufacturing Technology Management , Vol. 26 No. 3 , pp. 390 - 411 , doi: 10.1108/JMTM-08-2013-0102 .

Fink , A. ( 2014 ), Conducting Research Literature Reviews: From the Internet to Paper , [2014] , 4th ed. , SAGE , Thousand Oaks, CA , available at: https://search.library.wisc.edu/catalog/9910206565302121 .

Fontana , F. , Klahn , C. and Meboldt , M. ( 2019 ), “ Value-driven clustering of industrial additive manufacturing applications ”, Journal of Manufacturing Technology Management , Vol. 30 No. 2 , pp. 366 - 390 , doi: 10.1108/JMTM-06-2018-0167 .

Fox , S. ( 2014 ), “ Third wave do-it-yourself (DIY): potential for presumption, innovation, and entrepreneurship by local populations in regions without industrial manufacturing infrastructure ”, Technology in Society , Vol. 39 , pp. 18 - 30 .

Gartner ( 2019 ), “ Hype cycle ”, available at: https://www.gartner.com/en/documents/3947508/hype-cycle-for-3d-printing-2019 .

Gartner , J. , Maresch , D. and Fink , M. ( 2015 ), “ The potential of additive manufacturing for technology entrepreneurship: an integrative technology assessment ”, Creativity and Innovation Management , Vol. 24 No. 4 , pp. 585 - 600 .

Garza , J. ( 2016 ), Understanding the Adoption of Additive Manufacturing , Massachusetts Institute of Technology, available at: http://hdl.handle.net/1721.1/110892 .

Gupta , B.M. and Dhawan , S.M. ( 2018 ), “ Three dimensional (3D) printing: a scientometric assessment of global publications output during 2007-16 ”, DESIDOC Journal of Library and Information Technology , Vol. 38 No. 4 , pp. 238 - 245 , doi: 10.14429/djlit.38.4.12300 .

Hsu , P.-F. , Kraemer , K.L. and Dunkle , D. ( 2006 ), “ Determinants of E-business use in U.S. Firms ”, Routledge , International Journal of Electronic Commerce , Vol. 10 No. 4 , pp. 9 - 45 , doi: 10.2753/JEC1086-4415100401 .

Jirsák , P. and Brunet-Thornton , R. ( 2019 ), “ Perspectives of operational additive manufacturing case studies from the Czech aerospace industry ”, Journal of Eastern European and Central Asian Research , Vol. 6 No. 1 , pp. 179 - 180 , doi: 10.15549/jeecar.v6i1.273 .

Kianian , B. , Tavassoli , S. , Larsson , T. and Diegel , O. ( 2016 ), “ The adoption of additive manufacturing technology in Sweden ”, Elsevier B.V. , Procedia CIRP , Vol. 40 , pp. 7 - 12 , doi: 10.1016/j.procir.2016.01.036 .

Kuan , K. and Chau , P. ( 2001 ), “ A perception-based model for EDI adoption in small businesses using a technology-organization-environment framework ”, Information and Management , Vol. 38 No. 8 , pp. 507 - 521 , doi: 10.1016/S0378-7206(01)00073-8 .

Levy , Y. and Ellis , T. ( 2006 ), “ A systems approach to conduct an effective literature review in support of information systems research ”, Informing Science , Vol. 9 , pp. 181 - 211 , doi: 10.28945/479 .

Long , Y. , Pan , J. , Zhang , Q. and Hao , Y. ( 2017 ), “ 3D printing technology and its impact on Chinese manufacturing ”, International Journal of Production Research , Vol. 55 No. 5 , pp. 1488 - 1497 , doi: 10.1080/00207543.2017.1280196 .

Marak , Z. , Tiwari , A. and Tiwari , S. ( 2019 ), “ Adoption of 3D printing technology: an innovation diffusion theory perspective ”, International Journal of Innovation , Vol. 7 No. 1 , pp. 87 - 103 , doi: 10.5585/iji.v7i1.393 .

Martinsuo , M. and Luomaranta , T. ( 2018 ), “ Adopting additive manufacturing in SMEs: exploring the challenges and solutions ”, Journal of Manufacturing Technology Management , Vol. 29 No. 6 , pp. 937 - 957 , doi: 10.1108/JMTM-02-2018-0030 .

Mayring , P. ( 2000 ), Qualitative Inhaltsanalyse. Grundlagen und Techniken , Deutscher Studien Verlag , Weinheim .

Mellor , S. , Hao , L. and Zhang , D. ( 2014 ), “ Additive manufacturing: a framework for implementation ”, International Journal of Production Economics , Vol. 149 , pp. 194 - 201 , doi: 10.1016/j.ijpe.2013.07.008 .

Murmura , F. and Bravi , L. ( 2018 ), “ Additive manufacturing in the wood-furniture sector: sustainability of the technology, benefits and limitations of adoption ”, Journal of Manufacturing Technology Management , Vol. 29 No. 2 , pp. 350 - 371 , doi: 10.1108/JMTM-08-2017-0175 .

Neuendorf , K. ( 2017 ), The Content Analysis Guidebook , 2nd ed. , SAGE Publications , Los Angeles .

Niaki , M. and Nonino , F. ( 2017 ), “ Impact of additive manufacturing on business competitiveness: a multiple case study ”, Journal of Manufacturing Technology Management , Vol. 28 No. 1 , pp. 56 - 74 , doi: 10.1108/JMTM-01-2016-0001 .

Oettmeier , K. and Hofmann , E. ( 2017 ), “ Additive manufacturing technology adoption: an empirical analysis of general and supply chain-related determinants ”, Springer , Berlin Heidelberg , Journal of Business Economics , Vol. 87 No. 1 , pp. 97 - 124 , doi: 10.1007/s11573-016-0806-8 .

Oliveira , T. and Martins , M. ( 2011 ), “ Literature review of information technology adoption models at firm level ”, The Electronic Journal Information Systems Evaluation , Vol. 14 No. 1 , pp. 110 - 121 .

Rogers , E.M. ( 1983 ), Diffusion of Innovation , 3rd ed. , Free Press , New York .

Rousseau , D. , Manning , J. and Denyer , D. ( 2008 ), “ Evidence in management and organizational science: assembling the field's full weight of scientific knowledge through syntheses ”, The Academy of Management Annals , Vol. 2 No. 1 , pp. 475 - 515 , doi: 10.1080/19416520802211651 .

Rylands , B. , Böhme , T. , Gorkin , R. , Fan , J. and Birtchnell , T. ( 2016 ), “ The adoption process and impact of additive manufacturing on manufacturing systems ”, Journal of Manufacturing Technology Management , Vol. 27 No. 7 , pp. 969 - 989 , doi: 10.1108/JMTM-12-2015-0117 .

Sandström , C. ( 2015 ), “ Adopting 3D Printing for manufacturing – the case of the hearing aid industry ”, Adopting 3D Printing for Manufacturing – The Case of the Hearing Aid Industry , No. 262 , pp. 1 - 20 .

Sandström , C. ( 2016 ), “ The non-disruptive emergence of an ecosystem for 3D Printing - insights from the hearing aid industry's transition 1989-2008 ”, Elsevier , Technological Forecasting and Social Change , Vol. 102 , pp. 160 - 168 , doi: 10.1016/j.techfore.2015.09.006 .

Schniederjans , D. ( 2017 ), “ Adoption of 3D-printing technologies in manufacturing: a survey analysis ”, Elsevier , International Journal of Production Economics , Vol. 183 No. November 2016 , pp. 287 - 298 , doi: 10.1016/j.ijpe.2016.11.008 .

Schniederjans , D. and Yalcin , M. ( 2018 ), “ Perception of 3D-printing: analysis of manufacturing use and adoption ”, Rapid Prototyping Journal , Vol. 24 No. 3 , pp. 510 - 520 , doi: 10.1108/RPJ-04-2017-0056 .

Scott , R. ( 1995 ), Institutions and Organizations , S. Publications , Thousan Oaks , doi: 10.3917/mana.172.0136 .

Sculpteo ( 2019 ), “ The state of 3D printing ”, available at: https://info.sculpteo.com/the-state-of-3d-printing-report ( accessed: 5 January 2020 ).

Sheeran , P. and Webb , T. ( 2016 ), “ The intention–behavior gap ”, Social and Personality Psychology Compass , Vol. 10 No. 9 , pp. 503 - 518 , doi: 10.1111/spc3.12265 .

Snyder , H. ( 2019 ), “ Literature review as a research methodology: an overview and guidelines ”, Journal of Business Research , Vol. 104 , March , pp. 333 - 339 , doi: 10.1016/j.jbusres.2019.07.039 .

Soares Aguiar , A. and Palma-dos-Reos , A. ( 2008 ), “ Why do firms adopt E-procurement systems? Using logistic regression to empirically test a conceptual model ”, IEEE Transactions on Engineering Management , Vol. 55 No. 1 , pp. 120 - 133 .

Steenhuis , H.H. and Pretorius , L. ( 2016 ), “ Consumer additive manufacturing or 3D printing adoption: an exploratory study ”, Journal of Manufacturing Technology Management , Vol. 27 No. 7 , pp. 990 - 1012 , doi: 10.1108/JMTM-01-2016-0002 .

Steenhuis , H. , Fang , X. and Ulusemre , T. ( 2020 ), “ Global diffusion of innovation during the fourth industrial revolution: the case of additive manufacturing or 3D printing ”, International Journal of Innovation and Technology Management , Vol. 17 No. 1 , pp. 1 - 34 , doi: 10.1142/S0219877020500054 .

Teo , H.H. , Wei , K. and Benbasat , I. ( 2003 ), “ Predicting intention to adopt interorganizational linkages: an institutional perspective ”, MIS Quarter , Vol. 27 No. 1 , pp. 19 - 49 . available at: https://doi:10.2307/30036518 .

Tornatzky , L. and Fleischer , M. ( 1990 ), The Processes of Technological Innovation , Lexington Books , Lexington, MA .

Tranfield , D. , Denyer , D. and Smart , P. ( 2003 ), “ Towards a methodology for developing evidence-informed management knowledge by means of systematic review* introduction: the need for an evidence-informed approach ”, British Journal of Management , Vol. 14 , pp. 207 - 222 .

Tsai , C. and Yeh , C. ( 2019 ), “ Understanding the decision rules for 3D printing adoption ”, Technology Analysis and Strategic Management , Vol. 31 No. 9 , pp. 1104 - 1117 .

Venkatesh , V. and Davis , F. ( 2000 ), “ A theoretical extension of the technology acceptance model: four longitudinal field studies ”, Management Science , Vol. 46 No. 2 , pp. 186 - 204 .

Venkatesh , V. , Morris , M. , Davis , G. and Davis , F. ( 2003 ), “ User acceptance of information technology: toward a unified view ”, MIS Quarterly , Vol. 27 No. 3 , pp. 425 - 478 , available at: https://www.jstor.org/stable/30036540 .

Wang , Y. , Wang , Y. and Yang , Y. ( 2010 ), “ Understanding the determinants of RFID adoption in the manufacturing industry ”, Elsevier , Technological Forecasting and Social Change , Vol. 77 No. 5 , pp. 803 - 815 , doi: 10.1016/j.techfore.2010.03.006 .

Wang , Q. , Sun , X. , Cobb , S. , Lawson , G. and Sharples , S. ( 2016 ), “ 3D printing system: an innovation for small-scale manufacturing in home settings? – early adopters of 3D printing systems in China ”, International Journal of Production Research , Vol. 54 No. 20 , pp. 6017 - 6032 , doi: 10.1080/00207543.2016.1154211 .

Weber , R. ( 1990 ), Basic Content Analysis , 2nd ed. , SAGE Publications , California .

Weller , C. , Kleer , R. and Piller , F.T. ( 2015 ), “ Economic implications of 3D printing: market structure models in light of additive manufacturing revisited ”, International Journal of Production Economics , Elsevier , Vol. 164 , pp. 43 - 56 , doi: 10.1016/j.ijpe.2015.02.020 .

Westphal , M. ( 2018 ), The Promise of the Future: 3D Printing in Manufacturing , University of the Rockies, available at: https://0-search-proquest-com.biblioteca-ils.tec.mx/pqdtglobal/docview/2129666791/fulltextPDF/926FFF4E187C4A47PQ/1?accountid=11643 .

Wohlers Associates ( 2019 ), Wohlers Report 2019, 3D Printing and Additive Manufacturing State of the Industry, Annual Worldwide Progress Report , Wohler Associates , Fort Collins .

Wong , G. , Greenhalgh , T. , Westhorp , G. , Buckingham , J. and Pawson , R. ( 2013 ), “ RAMESES publication standards: meta-narrative reviews ”, Journal of Advanced Nursing , Vol. 69 No. 5 , pp. 987 - 1004 , doi: 10.1111/jan.12092 .

Yeh , C. and Chen , Y. ( 2018a ), “ Critical success factors for adoption of 3D printing ”, Technological Forecasting and Social Change , Vol. 132 , June 2017 , pp. 209 - 216 , doi: 10.1016/j.techfore.2018.02.003 .

Yeh , C. and Chen , Y. ( 2018b ), “ Technological forecasting and social change critical success factors for adoption of 3D printing ”, Technological Forecasting and Social Change , Vol. 132 , January , pp. 209 - 216 , doi: 10.1016/j.techfore.2018.02.003 .

Yoon , T. and George , J. ( 2013 ), “ Why aren't organizations adopting virtual worlds? ”, Computers in Human Behavior , Vol. 29 No. 3 , pp. 772 - 790 , doi: 10.1016/j.chb.2012.12.003 .

Further reading

Dwivedi , G. , Srivastava , S.K. and Srivastava , R.K. ( 2017 ), “ Analysis of barriers to implement additive manufacturing technology in the Indian automotive sector ”, International Journal of Physical Distribution and Logistics Management , Vol. 47 No. 10 , pp. 972 - 991 , doi: 10.1108/IJPDLM-07-2017-0222 .

Khorram Niaki , M. and Nonino , F. ( 2017 ), “ Additive manufacturing management: a review and future research agenda ”, International Journal of Production Research , Taylor & Francis , Vol. 55 No. 5 , pp. 1419 - 1439 , doi: 10.1080/00207543.2016.1229064 .

Kunovjanek , M. and Reiner , G. ( 2019 ), “ How will the diffusion of additive manufacturing impact the raw material supply chain process? ”, International Journal of Production Research , Taylor & Francis , Vol. 58 No. 5 , pp. 1 - 15 , doi: 10.1080/00207543.2019.1661537 .

Maresch , D. and Gartner , J. ( 2018 ), “ Make disruptive technological change happen - the case of additive manufacturing ”, Elsevier , (June 2017) , Technological Forecasting and Social Change , p. 119216 , doi: 10.1016/j.techfore.2018.02.009 .

Oettmeier , K. and Hofmann , E. ( 2016 ), “ Impact of additive manufacturing technology adoption on supply chain management processes and components ”, Journal of Manufacturing Technology Management , Vol. 27 No. 7 , pp. 944 - 968 , doi: 10.1108/JMTM-12-2015-0113 .

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Polymer 3D Printing Review: Materials, Process, and Design Strategies for Medical Applications

Polymer 3D printing is an emerging technology with recent research translating towards increased use in industry, particularly in medical fields. Polymer printing is advantageous because it enables printing low-cost functional parts with diverse properties and capabilities. Here, we provide a review of recent research advances for polymer 3D printing by investigating research related to materials, processes, and design strategies for medical applications. Research in materials has led to the development of polymers with advantageous characteristics for mechanics and biocompatibility, with tuning of mechanical properties achieved by altering printing process parameters. Suitable polymer printing processes include extrusion, resin, and powder 3D printing, which enable directed material deposition for the design of advantageous and customized architectures. Design strategies, such as hierarchical distribution of materials, enable balancing of conflicting properties, such as mechanical and biological needs for tissue scaffolds. Further medical applications reviewed include safety equipment, dental implants, and drug delivery systems, with findings suggesting a need for improved design methods to navigate the complex decision space enabled by 3D printing. Further research across these areas will lead to continued improvement of 3D-printed design performance that is essential for advancing frontiers across engineering and medicine.

1. Introduction

Polymer 3D (three-dimensional) printing has advanced rapidly in recent years with many areas of research now translating to engineered products, especially in medical fields [ 1 , 2 , 3 , 4 ]. Polymer printing is advantageous for a broad range of medical areas that benefit from the diversity of polymer material characteristics and processing approaches [ 5 , 6 , 7 , 8 ]. 3D printing is a highly desirable fabrication approach because it enables the construction of designs with complex geometries and architectures that are not possible with conventional manufacturing processes. For instance, tissue scaffold structures fabricated with polyjet and stereolithography printing can achieve hierarchical forms that mimic bone, thereby providing a mechanical and biological niche to support tissue regeneration [ 9 , 10 ]. Additionally, the selection of polymers has advantages over metal printing approaches, that result in metal implants that do not degrade in the body and lead to mechanical issues such as stress shielding [ 11 ]. In areas of safety equipment, polymer-printed lattices achieve efficient energy absorption with a rapid fabrication process that bypasses the supply chain limitations of bulk manufacturing [ 12 , 13 ]. Polymer printing is possible using extrusion, resin, and powder 3D printing processes that provide versatility for material selection and supporting designs with diverse architectures, responses, and layouts [ 14 , 15 ]. Because of the large design space offered by 3D polymer printing, and its opportunities for improving medical applications, we carried out a critical review by considering recent advances in materials, processes, and design strategies that all influence an application’s outcome [ 16 ], as illustrated in Figure 1 for a tissue scaffold example.

Material, process, and design considerations for medical applications, illustrated for a tissue scaffold example [ 17 ]. Images adapted with permission.

The Figure 1 schematic highlights a hierarchical tissue scaffold constructed from beam-based unit cells with interconnected considerations in materials, process, and design for ensuring appropriate mechanical and biological functioning [ 17 ]. In this example, a design strategy for mimicking the hierarchical structure of bone largely drove the need for a suitable printing process and material selection to support the application. The material choice was dictated by a need for appropriate stiffness to ensure structural integrity while retaining biocompatibility to promote tissue growth, which was fulfilled with a methacrylic acid-based polymer. The printing process requires the formation of layers for building the complicated hierarchical truss structure, which was achieved by stereolithography printing. However, once these factors are selected, there is a need to iterate and refine the structure’s design based on performance variability attributed to uncertainty and part variation in the 3D printing process [ 9 , 18 ].

Comparative studies for tissue scaffolds can achieve widely different design strategies based on different material/printing process decisions. For instance, tissue scaffolds constructed from polycaprolactone (PCL) using fused deposition modeling have more compliant structures with biodegradability, while titanium scaffolds printed with selective laser sintering have a higher stiffness, but no biodegradability [ 19 , 20 ]. These choices then influence the scaffold’s topological design, since it is generally not feasible to print polycaprolactone as a truss-based structure, whereas selective laser sintering processes are able to produce titanium in forms to achieve mechanically efficient truss-based structures that promote high porosity for large void volumes for tissue growth.

Generally, decisions across material, process, and design strategies occur in a nonlinear and integrated fashion that requires careful consideration and knowledge of their relation to an application. Here, materials, process, and design strategies for polymer printing are reviewed in the context of medical applications, with a critical assessment for how each of these decision factors influence applications and one another. Initially, materials are reviewed to highlight their capabilities and properties, with data presented to compare diverse materials available for mechanical applications. Reviews on printing processes include extrusion, resin, and powder printing, which are among the most common approaches for polymer printing, with considerations for how processing influences part fidelity and functionality. The investigation of design strategies provides an overview for organizing processed materials that is advantageous for tuning application performance. Considered applications include prosthetics, safety equipment, and drug delivery, which provide context for how fundamental research in these areas translates to medical scenarios. The review concludes with considerations and challenges for researchers to consider as polymer printing continues advancing, with a particular need for new methodological design approaches to improve engineering outcomes in medicine.

2. Material Capabilities

Material capabilities of polymers for 3D printing are informed by their molecular structures, and also depend on a material’s processing during printing. The selection of materials for design applications is often conducted by considering measurable properties, such as mechanical properties, with ranges based on processing and testing methods that provide further complications in predicting part performance during the system design.

2.1. Material Structure

There is a broad range of polymer materials for 3D printing, with capabilities informed from their molecular structure, with polymers processed in different manners for each printing process. In extrusion processes, thermoplastics are commonly used for 3D printing where they are melted for extrusion followed by hardening after deposition [ 21 ]. For example, acrylonitrile butadiene styrene (ABS) is a common thermoplastic that exhibits favorable impact strength and improved chemical resistance compared to pure polystyrene [ 22 ]. The properties of ABS are tunable based on the ratio of its three monomers, for instance, its density may range from 1.05 mg/m 3 to 1.07 mg/m 3 with resulting tensile moduli from 2.5 GPa to 2.7 GPa. Acrylonitrile styrene acrylate (ASA) is an alternative to ABS with improved heat resistance and exceptional ultraviolet stability [ 23 ], while polylactic acid (PLA) is another popular thermoplastic with biocompatibility but a lower glass transition temperature.

PLA is also suitable for further types of printing processes, such as resin curing with stereolithography [ 24 ], which enables the construction of more complex part architectures than is generally possible with extrusion processes. Although PLA is biocompatible, there is some concern for toxicity in stereolithography printed PLA because of the addition of photopolymers to the resin solution, which is necessary for cross-linking monomers to form polymers in the presence of ultraviolet light. However, when properly printed and post-processed, resin curing processes have been demonstrated as safe for medical applications, depending on the particular combination of chemical components [ 25 ]. These considerations for linking the chemical structure of a polymer to its functioning and printing are essential in pairing printing processes with materials to achieve a desired set of properties for a specified application.

2.2. Material Properties

There are diverse material properties necessitated by medical applications that are achievable through 3D printing. Often, medical applications drive the need for specific material capabilities, such as the need for energy absorbing materials in impact resistance, multicolored parts with suitable textures for modeling surgical anatomies, or specified material properties to mimic biological tissues. Figure 2 highlights recent research in medical polymer materials with a focus on mechanical capabilities for toughness [ 26 , 27 ] and flexibility [ 28 , 29 ], biological capabilities for biocompatibility [ 24 , 30 ], and further capabilities such as electrical conductivity [ 31 , 32 ].

Materials with highlighted properties for ( A ) toughness [ 26 ], ( B ) flexibility [ 28 ], ( C ) biocompatibility [ 30 ], and ( D ) conductivity [ 31 ]. Images adapted with permission.

Toughness in a material refers to its capability to absorb energy and plastically deform without fracturing, which is calculated from a combination of the material’s strength and ductility. Recently, a 3D-printed tensile bar with crosshatch structures was printed from a tough polyurethane material with comparisons including physically cross-linked Carbothane AC-4095A in pellet form and chemically cross-linked polyurethane with 68A hardness in liquid resin form ( Figure 2 A) [ 26 ]. Results demonstrated elastomeric polyurethanes are relatively tolerant of architectures and notches, which also promotes their use in a variety of design strategies. A further example of toughness for biomedical materials was demonstrated with a methacrylic polymer printed using the resin curing process with a tensile strength of 41 MPa and a general elongation up to 50% before breaking [ 27 ]. The material was used for printing a shaft coupling for an assembly without any post-treatment necessary due to the high accuracy of the printing process.

Flexible materials have been constructed recently that are useful as prosthetics, and enable the optimization of a specified form for a person’s unique physiology through scanning and fitting technologies ( Figure 2 B) [ 28 ]. The patient of interest for the study was 27 years old and had a topographic scan of their face that used 3D mapping software to print the nose shape using a Stratasys polyjet printer with TangoPlus flexible material. The TangoPlus material had a 26 to 28 Shore A Hardness, 0.8 to 1.5 MPa tensile strength, and 2 to 4 kg/cm tear resistance, while having a feel similar to rubber. Recoloring was necessary to match the patient’s skin tone. Flexible materials have also been used to print complex structures, such as an Eiffel tower model printed with temperature-stimulated flexible polymer printed using stereolithography [ 29 ]. The model distorts at lower temperature and as the temperature increases to 70 °C, the print regains its original form. This temperature-dependent functionality provides possibilities for medical applications with heat-initiated actuation, which could be initiated by body heat or devices.

Biocompatibility is a necessary material property for printed devices that interact with the body, such as hearing aids and retainers, or are implanted in vivo, such as artificial joints or tissue scaffolds. Depending on the application, biocompatibility can have differing criteria, but generally refers to the need for the material to do no harm to the body while facilitating its intended function. For tissue scaffolds, biocompatibility typically refers to a need for non-cytotoxicity, biodegradability, and promotion of tissue growth. Polyjet printing uses Stratasys MED610 material, which is an acrylic-based polymer that has recently had success for printing tissue scaffolds of complex topologies ( Figure 2 C) [ 30 ]. Biological testing was conducted by measuring cell viability using Saos-2 cells that survived, with no difference between the 3D-printed materials and controls after 48 h. Further testing demonstrated growth on tissue scaffold surfaces; however, the growth was limited compared to other tissue engineering materials. An alternative approach is the use of stereolithography for 3D-printed lattices using polylactic acid that can reliably form lattice structures with microscale features [ 24 ]. Further testing is required to determine the benefits of 3D-printed polymers to conventional tissue engineering approaches; however, polymers provide immediate advantages over metals due to their ability to degrade safely in vivo.

Electrical conductance is another material property that is useful for medical applications and has been used for fabricated, sensorized tissue analogues through the 3D printing of an organogel. The technology was used to create a suture training pad fabricated with embedded piezoresistive strain sensors and conductive threads as electrodes to quantify the performance of the trainee ( Figure 2 D) [ 31 ]. Fabrication steps included fixing nylon fabric to the bottom of a PLA mold, then pouring and curing skin-colored liquid PDMS, inserting conductive threads into the 3D-printed organogel, encapsulating sensors, adding a fat layer, and cutting the sample to form a suture pad. Further polymer electrical conductivity has been demonstrated with thermoplastics mixed with conductive carbon black fillers for 3D printing a chess rookie that enables turning on an LED light [ 32 ]. These printing capabilities enable new types of design applications that could provide feedback in different medical scenarios through embedding sensors in fabricated designs, possibly activating when certain mechanical triggers are reached.

2.3. Material Capabilities

Properties of 3D-printed parts are dependent on both their material structure and printing process, and therefore require extensive testing of combinations of materials/process parameters to determine material capabilities for a given application [ 33 ]. For instance, a part’s mechanical response when fabricated with fused deposition modeling is alterable based on the printed layer thickness, processing temperature, and orientation [ 34 , 35 ]. In Table 1 a summary is provided that highlights the measured mechanical properties of some common polymer 3D-printed materials tested as solid samples; additional notes in the table provide context for how testing was carried out to provide ranges of process-dependent properties. Material properties include strength- and stiffness-related metrics that are key properties for selecting suitable materials in mechanical applications.

Measured 3D-printed part properties organized by material and printing process. Further details included to provide relevant context.

In Table 1 , multiple studies are reported for comparisons of ABS materials that all demonstrated similar, but slightly different mechanical properties [ 34 , 35 , 36 ], such as tensile strength ranging from 15 MPa to 38 MPa. These differences are accounted for in part because of the different processing temperatures and printing parameters used to construct parts, the slightly different proportions of monomers in ABS’s structure, and the tested part’s orientation. For instance, the low tensile strength measurement of 15 MPa for ABS was due to testing in the transverse loading direction compared to the higher measurements of tensile strength closer to 30 MPa based on the build layer orientation. Similar differences were observed for polycarbonate materials based on their processing and chemicals used to manufacture the material [ 35 , 36 ]. One study concluded that a blend of polycarbonate referred to as a bio-based polycarbonate had a slightly higher strength of 65 MPa and significantly higher elastic modulus of 2100 MPa than a polycarbonate manufactured using fossil fuels with 62 MPa strength and 1500 MPa elastic modulus [ 35 ]. Polyether ether ketone (PEEK) and polylactic acid (PLA) are commonly used biocompatible materials with relatively high mechanical strength and stiffness among polymers, and are also manufacturable with fused deposition modeling [ 37 , 38 ]. PEEK is generally the more expensive of the two materials with an elastic modulus up to 4100 MPa, while PLA has an elastic modulus of 4400 MPa; both are the highest values among the surveyed Table 1 materials.

Numerous 3D-printed biocompatible materials have been recently investigated for use as bone tissue scaffolds, with several methacrylic/acrylic-based materials included as examples in Table 1 [ 9 , 16 , 17 ]. These materials were printed with varied resin curing processes and all demonstrated similar elastic moduli around 1500 MPa to 2000 MPa, with some dependency on build orientation. In comparison to the fused deposition modeling parts, these resin prints have a lower stiffness, although their stiffness is tunable based on the curing time per layer and post-processing curing time that has been demonstrated for lattice structures [ 16 ]. Overall, the highlighted materials from Table 1 demonstrate how a single material can achieve varied properties based on its processing, and that varied processes enable material selection with similar property ranges. Further considerations for selecting a material/process combination are fabrication accuracies and consistency, which further add complexity to design decisions when selecting a 3D printing approach for a given application.

3. Printing Processes

The most common techniques for polymer 3D printing include extrusion-, resin-, and powder-based processes ( Figure 3 ) [ 1 ]. Each type of process enables the additive deposition of layers to form parts and carries out fabrication using unique processing steps that restrict processes to different material selections and capabilities to form designs.

3D printing schematics for ( A ) fused deposition modeling, ( B ) stereolithography, and ( C ) selective laser sintering that are representative of extrusion, resin, and powder processes, respectively.

In extrusion processes such as fused deposition modeling, the material is melted and extruded through a nozzle where it is directed for deposition to form part layers ( Figure 3 A) [ 41 , 42 ]. The filament feed generates nozzle pressure that is used to control material flow during part construction. In direct ink writing, which is another extrusion process, material is pushed through a nozzle according to an applied external shear stress such as air pressure or piston movements [ 43 ]. Resin 3D printing relies on applying ultraviolet light in specified patterns to form a part layer by layer by curing deposited liquid resin, which is commonly used for stereolithography printing [ 44 , 45 ]. In direct laser writing, ultraviolet light is directed towards a vat of photosensitive resin to form solid layers with a moving build platform ( Figure 3 B). Resin curing also occurs in polyjet printing, with the deposition of ink/resin on a surface with subsequent ultraviolet curing [ 9 , 17 ]. Powder 3D printing relies on fusing powders of a selected material using lasers in selective laser sintering [ 46 , 47 ] ( Figure 3 C) or by chemical means in binder jetting. In these processes a bed of powder is solidified and replenished layer by layer to form a part.

3.1. Extrusion

Among extrusion 3D printing processes, fused deposition modeling is the most commonly used ( Figure 3 A) [ 41 , 42 ]. In fused deposition modeling material is fed into the printer as a continuous filament. The extruder body is heated to melt the filament that is extruded by the pressure generated by the filament feed. After filament extrusion, the filament cools down and solidifies to form a solid geometry. Some of the most common printing materials for fused deposition modeling are polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), polyethylene terephthalate (PET), and thermoplastic polyurethane (TPU). Support materials are also available that are removed during post-processing and include water-dissolvable materials such as polyvinyl alcohol (PVA), breakaway materials, and wax. The performance of the printed parts depends on material selection and process parameters such as layer thickness, build orientation, raster angle, infill density, nozzle temperature, and printing speed [ 48 ]. In fused deposition modeling, the nozzle temperature is generally maintained at a few degrees higher than the melting point of the polymer, since further increasing the nozzle temperatures may affect the performance for materials like PEEK and polyetherimide (PEI). It has been reported that the elongation percentage before failure and impact strength of a PEI part starts reducing when the temperature increases beyond an optimal nozzle temperature [ 49 ]. On the other hand, lower temperatures may result in extrusion difficulty and poor print quality due to the formation of porous volumes between the layers [ 49 ]. Additionally, layer size presents trade-offs in print resolution, part performance, and printing speed while resulting in variable amounts of anisotropy in final part properties introduced by patterning of layers in specified directions.

Direct ink writing, also known as robocasting, is another extrusion 3D printing process that avoids the heating requirements of fused deposition modeling, and rather deposits a shear, thinning viscoelastic material via a nozzle by applying external shear stress [ 50 , 51 , 52 ]. Since the process enables printing in ambient conditions, it is ideal for printing soft materials. As the shear stress increases, the viscosity of the ink reduces and enables extrusion through the nozzle. As the ink is extruded, it regains its viscosity to form a 3D structure. The filaments are stacked to additively form the final part. The printed part is cured in a different environment as per the material requirement. Direct ink writing is used to print different materials including bio-inks [ 43 ], fiber-suspended inks [ 50 , 53 ], electro/magnetic inks [ 54 ], and multi-material inks [ 55 ]. The capability of printing different materials in direct ink writing has made it possible to produce designs for diverse applications [ 50 , 52 ]. Some of the most widely used polymers for direct ink writing are polydimethylsiloxane (PDMS), thermoplastics, and epoxy. The major factors in determining the printability are the viscosity and shear thinning property of the material.

3.2. Resin Curing

Resin 3D printing processes expose photosensitive monomers to controlled ultraviolet light or other high energy light sources [ 56 ]. Resin curing processes typically benefit from high resolutions and quality part finishing in comparison to other printing methods in comparable price ranges. Ultraviolet curing strategies include stereolithography with direct laser writing (SLA; Figure 3 B), digital light processing (DLP) [ 57 , 58 ], continuous liquid interface production (CLIP) [ 58 ], and continuous digital light manufacturing (CDLM) [ 59 ], which all have varied strategies of exposing a vat of resin to light to form a part. Stereolithography printing with direct laser writing includes a resin tank, a high energy light source, and a reflecting mirror to control the resin exposure to a laser. The resin in the tank is exposed to a computer-controlled laser that solidifies the resin to form a solid layer. After exposure to one layer, the printing platform moves vertically for printing the next layer [ 56 ]. After all the layers are printed, the part is washed and cured under ultraviolet light to strengthen the structure, which provides fine tuning for specific applications [ 60 ]. The duration of curing alters the printed part mechanics, for instance, when comparing parts that were post-cured for 30 h to those that had no post-curing, the post-curing with ultraviolet light was more time-efficient and improved mechanical properties, such as elastic modulus, and promoted material homogeneity through higher crosslinking [ 61 ]. Though stereolithography printing has a high resolution and printing speed, in general, it lacks multi-material printability.

Polyjet (also known as inkjet) printing is an alternate resin curing process that uses a nozzle to deposit droplets of material that are immediately cured by an ultraviolet beam upon deposition to form a layer [ 62 ]. Polyjet printing is advantageous for printing multimaterial models rapidly with multi-nozzle jetting, which also enables printing with support materials [ 63 , 64 ]. However, materials should generally still have shear thinning properties, which limits availability [ 58 ]. Inkjet printing has applications in fields ranging from prototyping to electronics to bio-printing [ 62 , 64 ], and has been demonstrated recently for use in biomedical devices using mechanically efficient lattice structures [ 9 ]. Lattices were printed using a network of beams with diameters of approximately 400µm, with fabrication defects depending on topology design and build direction. Further studies are required to determine whether polyjet printing is suitable for tissue engineering applications, with a need to further demonstrate its capabilities by producing structures with cell seeding and proliferation capabilities [ 30 ]. However, the technology provides a potential for the rapid fabrication of large sets of structures that are customizable for specific patients in applications such as safety equipment.

3.3. Powder Fusion

Powder fusion processes rely on depositing powder layers that are either melted or bonded to additively fabricate parts. Two common powder fusion techniques for polymer printing are selective laser sintering and binder jetting [ 65 ]. Figure 3 C demonstrates the working principles of selective laser sintering, which relies on a powder stock leveled to enable fusion of one layer through exposure to a laser that follows a specified path. Once a layer is printed, the platform is lowered, and the process is repeated. One of the major advantages of selective laser sintering is the leftover powder in the platform acts as a support during part construction. Therefore the process does not require printing a separate support material and enables complex part and assembly fabrication [ 47 ].

In binder jetting printing, a jetted material binds powder as an alternative to laser melting [ 65 ]. The powder is spread on the printing platform within a predetermined thickness and then the binding material is injected to form a bonded layer. The binder jetting technique uses multiple nozzles to inject the binding material, which is potentially faster than laser melting. Binder jetting is generally an efficient process capable of printing multicolor, multi-material, and functionally graded materials [ 66 ]. Since the binding material acts as an adhesive to hold the powder together and form a printed geometry, the achieved properties of the printed parts depend on the binding material in addition to the shape and size of the powder [ 67 , 68 ].

4. Design Strategies

Design strategies that are application independent provide a means for 3D-printed parts to support a desired functionality that extends beyond simply printing a solid part. Investigated strategies are presented in Figure 4 including (A) architected materials [ 14 ], (B) responsive polymers [ 15 ], (C) multi-material combinations [ 69 ], (D) functionally graded materials [ 70 ], and (E) customization [ 71 ], which all provide a means for improving the functionality and performance of printed parts. These layout strategies are beneficial for medical applications because they provide further refinement of functionality and properties for designed devices beyond the selection of materials and printing processes.

Design strategies including a (A) hierarchical architected lattice [ 14 ], ( B ) thermo-responsive container [ 15 ], ( C ) multi-material structure [ 69 ], ( D ) functionally graded lattice [ 70 ], and ( E ) customized mandible template [ 71 ]. Images adapted with permission.

4.1. Architected Materials

Architected materials are designed structures engineered with a regular patterning of subunits, such as a lattice with unit cells following a designated topological distribution that takes advantage of organized material placement to improve mechanical properties for a given structural density. The synthesis of architected materials has become efficient with the emergence of 3D polymer printing that enables the printing of complex geometries with high resolution and precision [ 72 ]. The material organization throughout the structure determines the properties of the part, which are scaled from the base material properties used to construct the architected structure [ 73 , 74 ]. Constructing architected materials opens the possibility to engineer designs across a wide range of elastic modulus and density values through considering varied strategies for topological material distribution.

Stretch dominated beam-based lattice structures are architected materials commonly used for carrying loads in medical applications due to their high mechanical efficiency, although bending-dominated foams are also desirable for their energy absorption properties [ 75 ]. Patterning unit cells provides a simple way of configuring an architected material by first designing a single unit cell consisting of beams and then placing unit cells adjacent to one another to form a lattice structure [ 76 ]. The beam diameter and the topology of beams within a unit cell additionally inform the biological functionality of the architected material [ 77 ], such as supporting mechanobiological processes for tissue growth [ 5 ]. Hierarchical strategies are a more sophisticated approach to producing architected materials for improved mechanics [ 78 ], and are demonstrated in Figure 4 A for a tissue scaffold application [ 14 ]. Hierarchical architected materials have a moderately decreased elastic moduli and a highly increased nutrient transportation capability due to larger pores introduced by the hierarchy [ 17 ], therefore providing potential performance improvements for tissue scaffolds in regenerative medicine applications.

4.2. Stimuli-Responsive

Stimuli-responsive designs rely on the coordinated placement of printed parts that have a directed state change when an external stimulus, such as light, heat, or force, is applied [ 79 ]. The application of external stimuli modulates the energy in the system that drives a desirable mechanical action [ 80 ]. A common strategy for stimuli-responsive parts is the combination of contrasting materials with different reaction levels to a stimulus. The combined material reactions throughout the system provide a directed response, as exemplified in Figure 2 B with a combination of shape memory polymers to form a self-folding box [ 15 ]. Thermal energy was used to drive shape changes on the basis of the time-dependent behaviors of each polymer to close the box. Additionally, a single material may be cleverly distributed throughout a system to react with shape memory to form different shapes according to stimuli. Key considerations in stimuli-responsive material design are how the mechanics and interaction of materials control the change in part shape and the duration of time for responses when external stimuli are applied.

The combination of materials with contrasting levels of response to stimuli was explored recently with a glassy polymer coupled with an elastomer using extrusion printing to form a rod shaped structure [ 81 ]. Here, the glassy polymer was more prone to change shape in response to external heating stimuli. The result demonstrated that by carefully tuning the stimuli the thermomechanical behavior can induce more than 300% of the failure strain. High-resolution and high-contrast microdisplays have also been used for high-resolution photocuring that has enabled the manufacturing of 3D-architected photo-shape memory alloys [ 82 ]. In one of the studies, a new 4DMesh method was introduced using a thermoplastic actuator for shrinking and bending a 4D print to form a non-developable surface [ 83 ]. The study also validated the aesthetic, mechanical, and geometric properties of the print and demonstrated its application in industrial packaging and molds.

4.3. Multi-Material

Multi-material 3D prints are increasingly investigated for improving overall functionality and performance in printed parts through combining materials with contrasting properties [ 64 ]. Multi-material printing has been commonly incorporated with printing processes including fused deposition modeling, direct ink writing, and material jetting. Multi-material printing uses either a single nozzle extrusion that prints materials one at a time, or a separate nozzle for each material [ 50 , 84 ]. The mechanical properties of multi-material periodic composites are unique compared to single-material structures. For instance, fused deposition modeling has been used to combine a stiff periodic structure embedded in a hyperplastic material to reach a high compliancy and rate of strain recovery [ 85 ]. The performance was achieved through the embedded highly flexible matrix facilitating a uniform distribution of the applied load throughout the periodic structure, therefore enhancing the overall mechanical response. Multi-material printing has also been used to fabricate medical phantoms that reproduce mechanical properties of biological tissues while recreating anatomically accurate models [ 86 ].

The possibility of multi-material 3D printing for a functional and shape-morphing structure using direct ink writing has been recently demonstrated [ 87 ]. Multi-material printing is also advancing the field of metamaterial printing through the use of a tunable negative Poisson ratio for a uniform cell structure ( Figure 4 C) [ 69 ]. Instead of using the geometric parameters to control the Poisson ratio, the application of different elastic behaviors of the printed material was demonstrated by printing the beams with flexible and rigid polymers [ 69 ]. The multi-material technique has also been applied in the field of fiber-reinforced composites printing, where fiber orientation in a polymer-fiber composites was studied using direct ink writing [ 50 ]. In this study, an epoxy-resin-based ink was proposed to control the fiber orientation and demonstrated an up to 10-fold improvement in mechanical strength. Multi-material printing with multiple nozzles is an efficient and fast way of printing multiple materials simultaneously. A direct ink writing multi-material and multi-nozzle print head is able to print up to eight different types of material, with capabilities of controlling deposition of each material at the scale of individual voxels that enables printing parts for diverse applications [ 84 ].

4.4. Functionally Graded

Functionally graded materials are architected materials that have been engineered with a gradual geometric or material transition throughout the structure [ 88 ]. Functionally graded materials prevent the drastic transition of mechanical properties at interfaces and provide a smooth transition of properties. Thus, functionally graded materials mitigate stress concentrations over interfaces and provide durability, especially as load-bearing supports [ 89 ]. Functional gradients are additionally useful in medical applications since they provide a complex structural diversity of bioinspired gradients and facilitate more control over fluid flow, mass transport, biodegradation, and mechanical properties, such as stiffness, strength, and hardness, throughout a designed structure, which are beneficial for biomedical implants [ 90 ].

Figure 4 D shows a functionally graded lattice structure where beams have varied thicknesses based on their location [ 70 ]. The structure is lightweight and has excellent energy absorbing capabilities due to its deformation behavior. The structure demonstrates deformation at the lowest density layer first, and then the deformation continues with a layer-by-layer collapse in sequence, except for the last layers that collapse concurrently or very shortly after one another. This sequential deformation of layers is enabled by the density gradient and provides desirable behaviors in mechanical responses for applications where sudden mechanical failures are a concern.

4.5. Customization

Customization enables printing parts with geometries altered on a per-print basis that is particularly useful for patient-specific fabrications for personalized medicine, where the configured layout matches a specific patient’s anatomy. For instance, in bone tissue engineering, implant devices are printable based on the patient’s bone geometry that has been imaged and provides a better interface to improve host-bone compatibility [ 91 ]. Such customization is additionally important for dental implants to ensure proper fits [ 92 ]. Customized layouts are also used for model printing that is representative of a patient’s unique physiology, as demonstrated in Figure 4 E for a 3D-printed mandible model made of polylactic acid [ 71 ]. The printed model can aid in complex mandibular reconstruction by providing the opportunity of planning medical operations using the physical part. Planning using the 3D-printed model can help in improving the quality and precision of surgery, while also providing overall time savings.

Manual customization is often cumbersome due to the number of layout possibilities to consider when fitting a part for a patient, which is why image-based techniques are commonly applied for automated design customization [ 93 ]. For instance, a set of 2D images of a patient’s CT scan are converted to a 3D image. Then, 3D imaging data is converted to virtual 3D surface shape that is matched with optical scan data to form a 3D-printed object blueprint or a variety of other imaging techniques [ 94 ]. Further strategies by engineers can use imaging data combined with optimization techniques to create 3D-printed parts that are fine-tuned for a patient’s specific geometry, while also improving performance in comparison to traditional manufacturing processes.

5. Medical Applications

The consideration of materials, processes, and design strategies enables tailored 3D-printed part fabrication, which is particularly beneficial for the medical industry. Throughout Section 5 , we consider how recent advances in polymer 3D printing are enabling new capabilities in medicine as demonstrated in Figure 5 for a (A) spinal fusion cage [ 95 ], (B) dental model [ 96 ], (C) prosthetic hand [ 97 ], (D) personal protection equipment [ 12 ], (E) sacral surgery planning [ 8 ], and (F) drug-delivering microneedles [ 98 ].

Medical 3D printing applications for ( A ) spinal fusion cage [ 95 ], ( B ) dental model [ 96 ], ( C ) prosthetic hand [ 97 ], ( D ) personal protection equipment [ 12 ], ( E ) sacral surgery planning [ 8 ], and ( F ) drug-delivering microneedles [ 98 ]. Images adapted with permission.

5.1. Tissue Scaffolds

3D polymer printing has recently gained interest in tissue engineering applications, where materials, process, and design strategies all play a role in the tailoring of scaffold structures [ 1 ]. Polymeric scaffolds are used in tissue engineering for synthesis of organs and have a primary purpose of restoring function or regenerating tissues [ 99 , 100 ]. Targeted tissues include bone, cartilage, ligament, skin, vasculature, neurons, and skeletal muscle. 3D printing is beneficial to provide personalization to patients and produce structures that are fine-tuned for clinical applications through efficient modular designs [ 101 ].

Scaffold optimization and design tuning is challenging, and in the case of bone tissue engineering, also requires the tuning of both biological and mechanical characteristics [ 102 ]. There is also the need to consider scaffold features across scales, such as hierarchical networks of pores for tissue growth and nutrient transport, with topology optimization as a commonly used configuration approach [ 10 ]. Figure 5 A demonstrates a 3D-printed scaffold created with polyjet printing configured from the investigation of multiple topology layouts, beam diameter sizes, unit cell sizes, and localized reinforcements for spinal fusion applications [ 95 ]. The study used a computational approach to compare relative trade-offs among designs to find viable scaffold configurations for bone growth. Further works have investigated trade-offs using tissue growth simulations and considering asymmetric unit cell structures generated with computational design [ 5 , 76 ]. Computational design and automated approaches are generally useful for 3D printing applications in medicine, since designs often benefit from unique configurations for specific patients.

5.2. Dental Implants

There are about 276 million persons throughout the world that suffer from tooth loss and could benefit from new solutions for dental implantation [ 103 ]. The emergence of 3D-printed polymers has provided economic and precise dental implants. In these treatments, 3D-printed polymers, such as polylactic acid, are fabricated and implanted in an oral cavity since they are resistant against impact and are non-toxic [ 104 ]. 3D-printed polymers also have little surface roughness, which is beneficial since surface roughness promotes biofilm formation that attracts harmful bacteria to the implant [ 105 ]. Figure 5 B demonstrates a polymer dental cast using polyjet printing from a study that compared 3D-printed dental casts to those made of dental stone; the 3D-printed cases were investigated with multiple printing processes and materials [ 96 ]. Results demonstrated that polyjet and stereolithography printing processes provided accuracies similar to conventional dental stone implants, with differences of means in measurements on x, y, and z axes being generally less than 15 µm for the best prints.

3D-printed polymers are implemented as crowns and bridges for provisional and fixed dental restoration. Fabricated crowns and bridges provide a low amount of internal discrepancies while also providing accurate occlusal fits [ 106 ]. Previously, metal structures were used as removable denture components and frameworks, but recently, PEEK polymers have replaced metals because of their high mechanical resistance with good biocompatibility [ 107 ]. Recently, researchers and medical professionals have developed and successfully implanted a patient-specific 3D-printed biopolymeric tooth [ 108 ]. The tooth was customized to the patient and provided further advantages of being high quality and low cost.

5.3. Wearable Prosthetics

3D printing offers a wide variety of approaches for new prosthetics that benefit from a range of material availability and customization for a person’s needs. In Figure 5 C, a 3D-printed prosthetic hand is demonstrated that is a combination of PLA and ABS materials for children with upper limb issues [ 97 ]. The wearable hand is low cost and provides a broad range of motion for users. Stretchable prosthetics with embedded actuators, signal processors, and sensors have also been tailored for individuals [ 109 , 110 ]. For instance, a smart wearable therapeutic device was fabricated with an embedded temperature sensor and programmable heater for self-activation according to a patient’s body temperature [ 111 ]. Recently, a pressure sensor-integrated 3D-printed elastomer-based wearable device was developed [ 112 ]. The device detects and monitors human body movement, external pressure, and the direction of external forces, which implies its potential as an electronic skin.

Every year, hundreds of thousands of people suffer from spinal cord injury around the world who could benefit from prosthetics [ 113 ]. Spinal cord injury can affect hand-function and locomotion. A polylactic acid (PLA) based 3D-printed wearable hand orthosis has been designed and fabricated to aid patients [ 114 ]. The device acts on the electromyography signal and works for the grasping function of the patient. Bone fracture is another prevalent medical problem where high density polyethylene (HDPE) or polypropylene (PP) based 3D-printed personalized wearable casts have been proposed and implemented for successful bone recovery [ 115 ].

5.4. Safety Equipment

The 2020 COVID-19 pandemic has elevated the importance of polymer 3D-printed safety equipment, as the conventional safety equipment supply was inadequate in certain regions when the need for personal protection equipment vastly exceeded demand. Polypropylene 3D-printed particle filters and masks were proposed as an alternative resource to help meet demand and avoid supply chain issues [ 116 ]. Additionally, in one study, a 3D-printed respirator was developed using TPU, ABS, and PLA filaments [ 13 ]. This respirator was reusable, easy to clean, and usable with an arbitrary number of filtration units. Figure 5 D demonstrates a 3D-printed helmet for use as personal protection equipment [ 12 ]. The primary helmet component integrates a breathing filter with a conventional safety helmet to provide an efficient means of creating safety equipment locally.

Studies have confirmed that 3D-printed architected materials are usable as helmet liners for protection from head injuries and provide advantageous energy absorption performance [ 117 ]. The energy absorption capabilities are tunable by using functionally graded materials. Architected helmet liners perform well for the multi-impact loading that is commonly experienced during motorcycle crashes [ 118 ]. Helmet testing has demonstrated that the liners have achieved standards for impact testing, while design variations in hole sizes provide tuning for optimal performance.

5.5. Surgical Planning

Surgical planning models have been 3D printed with rigid plastics including PLA and ABS for visualizing patient-specific organ models prior to operation. 3D-printed organ models are fabricated on a patient-specific basis at low cost, and have been applied in several medical fields including cardiology [ 119 , 120 ], neurology [ 121 , 122 ], urology [ 123 , 124 ], and osteology [ 8 , 125 ]. Figure 5 E demonstrates a patient-specific 3D-printed sacral model using PLA [ 8 ]. This model is used for refining techniques for sacral anomalies and for training new surgeons.

ABS filaments have been used in cardiology to fabricate the anatomical structure of patient-specific hearts for improving inflow in a device implantation procedure [ 120 , 126 ]. Studies have also fabricated anatomically accurate 3D-printed models for the pulmonary trunk and ventricular outflow tract using thermoplastic polyester resins [ 127 ]. PLA filaments and photosensitive liquid resins have been used to fabricate 3D-printed aneurysm models with hollow craniums and rigid walls [ 121 , 122 ]. These aneurysm models replicate patient-specific anatomies used to study the hydrodynamics in the system. Rigid photopolymers have been implemented to fabricate 3D-printed kidney models and prostates [ 123 , 128 ]. Patient-specific modeling was also conducted for a kidney with a removable tumor [ 129 ]. As a whole, these printing applications provide surgeons a way to experience and plan a surgery in a minimally invasive way prior to performing an actual surgery.

5.6. Drug Delivery

3D-printed drug delivery enables the fabrication of drugs for patient-specific needs, uniform drug distribution, and solvent-free drug-containing material production [ 130 ]. 3D-printed polycaprolactone and tricalcium phosphate meshes have demonstrated that micro-architecture influences drug delivery efficacy [ 131 , 132 ]. In vivo and in vitro studies demonstrate that these drug delivery constructs are resistant against Gram-positive and Gram-negative bacteria, while also potentially delivering a higher percentage of the incorporated drug to the body.

Drug delivery is also possible through application of 3D prints outside of the body. Figure 5 F demonstrates a 3D-printed microneedle array that drives drugs directly through the skin for microcirculation in the body [ 98 ]. These delivery approaches generally remain pain-free while promoting efficient transport that requires sophisticated geometric fabrication at a microlevel enabled by 3D printing. The microneedles are fabricated with a tip width between 65 and 84 µm, a pitch of 700 µm, and heights between 422 and 481 µm.

Polymeric 3D printing is also applied for fabricating drug delivery systems with multi-active dosage forms [ 133 ], time-tailored release tablets [ 134 ], and multilayer caplets [ 135 ]. The technology has been demonstrated for personalized drug delivery that can control release rate, drug combination, and dosing intervals [ 136 ]. Dosing requirements vary in patients based on their physiological functioning, which motivates personalization to improve patient responses. 3D-printed polymeric microcapsules and nanocapsules remain stable in the liquid suspension and biological fluids that improve drug efficiency [ 137 ], thereby motivating their use for controlled drug release.

Although there are numerous successes in translating polymer 3D printing research to medical applications, many challenges remain. Some of the key considerations in advancing research in material, process, and design strategies for 3D polymer printing are presented in Figure 6 , which additionally includes highlighted areas for researchers to address. Although issues are separated by material, process, and design, there is a large overlap between factors, such as material properties being influenced by process parameters and design performance being dependent on fabrication consistency. The development of new, integrated design strategies and computational approaches that holistically consider materials, process, and design in the development of new products is therefore essential for improving 3D-printed polymer performance in medical applications.

Key research challenges for 3D printing polymers using materials, process, and design strategies for medical applications.

Though numerous 3D printable polymer materials have been developed in recent years, it is not always straightforward to select materials for specified applications. Material selection is a critical aspect of achieving a print with desirable properties, but it is challenging, since 3D-printed polymers have uncertainty and ranges in performance based on the printing process and parameters [ 138 ] For example, parts printed with fused deposition modeling exhibit anisotropy with higher mechanical strength in the printing direction compared to the transverse direction [ 49 ]. Additionally, applications often require materials with multiple properties, such as the need for highly stiff materials for bone tissue engineering that also exhibit biocompatibility for tissue engineering [ 1 ]. The need for both material capabilities simultaneously limits the scope of possible materials. For instance, the polymer selected must have suitability for in vivo implantation, which creates difficulties when considering photopolymer resins that are potentially toxic if not fully cured, and many other materials with desirable mechanics that are not biocompatible.

Design strategies can help mitigate these material deficiencies, possibly through combining two contrasting biocompatible materials or through clever architected configurations to reach an overall beneficial, global system performance. When combining materials or considering the development of new materials, there are rigorous requirements of testing and validation of the printed parts, especially when considering the need for testing for all print process parameter combinations [ 139 ]. Improved modeling approaches could aid in predicting part performance to reduce the number of experiments required, which could benefit from new computational approaches and efficient design of experiments.

Research and advancement of suitable printing processes is also necessary due to the limitations and trade-offs associated with each 3D printing approach. For instance, there is an inherent trade-off for all printing processes between resolution, printing speed, and fidelity that affects the manufacturing logistics, mechanical performance, and surface finish of printed parts [ 140 , 141 ]. Generally, as resolution is increased, the quality of a part improves but requires more time to print. The trade-off between resolution and printing time differs on the basis of the printing process. For instance, processes such as fused deposition modeling and direct laser writing stereolithography, which have to follow a specified path to solidify each layer of a part, have print duration scaling according to part size and number of parts, whereas stereolithography processes, which project light that solidifies an entire layer of material at once in a resin vat, have print duration scaling with only the height of the part. Post-processing such as washing, curing, and support removal can also affect production times and performance [ 61 ]. In general, fused deposition modeling and selective laser sintering do not require washing and curing, while stereolithography requires washing to remove liquid resin and post-curing that additionally affects part mechanics. Support material removal can also increase part finishing time, resulting in broken parts or reduced performance from introduced cracks. Selective laser sintering and other powder processes may require brushing to remove excess powder from parts, which increases post-processing time and requires extra equipment such as powder removal stations. Further advancements in multi-nozzle printers could potentially improve production speeds while retaining part quality, or as the technology continues maturing, prices could drop that enable greater amounts of simultaneous part printing, since some printing speeds are limited by physics dictating how fast materials can be deposited and solidified, which often cannot be increased to improve speeds for single part production.

Printing reliability is crucial for ensuring parts operate as expected and to reduce the need for disposal of parts printed with major defects. Printing processes can introduce defects related to residual stress [ 142 ], porosity [ 143 ], and impurities [ 144 ]. The residual stress can cause permanent deformation, warping, delamination of layers, and lack of adhesion to the build plate. These process limitations indicate that research studies should be done on reducing fabrication errors that can lead to the near-error-free fabrication of 3D-printed polymer parts suitable for sensitive microscale medical devices. Further, for ABS fused deposition modeling parts, build orientation has been additionally demonstrated to influence reliability, which means design decisions for part layouts will also affect print process outcomes [ 145 ]. Fabrication errors can emerge from layouts of filament to form solid structure and are subject to greater possibility of failure around areas that lead to stress concentrations or geometries, such as holes that lead to the poor bonding of filaments.

The selection of a printing process for an application is also driven by their capabilities for printing specified geometries [ 138 ]. In general, extrusion processes have difficulties in forming overhangs that are necessary for many lattice structures, resin processes require support material or self-supporting geometries [ 146 ], while powder processes have unused powder that supports parts during printing to promote complex geometry formation [ 47 ]. Some support material strategies in extrusion printing and polyjet printing enable more complex part geometries than otherwise possible, but require further post-processing and the possibility of damaging prints during support removal [ 147 ]. Fused deposition modeling tends to be a cheaper process that can produce parts with sound mechanical performance [ 148 ]. Direct ink writing is suitable for printing in ambient conditions [ 50 ], although it has limitations in tuning part performance during printing since there is no heating involved. Resin printing processes are known for having high resolution, surface finish, and printing speed [ 138 , 149 ]. However, stereolithography printing lacks multi-material functionality, while polyjet printing leads to inconsistent surfaces for parts printed at its resolution limits [ 30 ]. Powder printing techniques are suitable for printing entire assemblies with powder acting as support, while being generally more expensive and having resolution limits based on the powder particle size. These considerations suggest a need for suitable design strategies for applications that maximize performance for prints based on the strengths and limitations of each printing process, and a need for improved printing processes and methods to bypass the inherent deficiencies of each process.

The design strategy selected for an application requires consideration of available materials and processes in relation to the medical application of interest. If complex geometries are necessary for constructing lattices or anatomically complex models, then stereolithography or powder processes may work best. If multiple materials are necessary, then fused deposition modeling or polyjet printing may be the most appropriate. Although it is possible to print complex geometries with multiple processes, it is difficult to determine an optimized configuration because of the wide-ranging possibilities in material choice, process parameters, and design decisions. For instance, a multi-material lattice structure may consist of thousands of beams that could all have individually specified materials and diameters [ 150 ]. New computational and experimental methods are necessary to aid design approaches for finding optimal solutions and fully leveraging 3D printing technologies [ 9 ]. Some researchers have developed Voronoi lattices to introduce bio-mimetic morphological scaffolds that can be fabricated by polymer 3D printing, which necessitates new tools for tuning Voronoi lattices for specific trade-offs [ 151 , 152 ]. Further considerations that could improve design opportunities are combinations of varied strategies, such as architected multi-material strategies or the creation of 3D-printed assemblies from different processes to leverage the strengths and limitations of each approach.

Engineering design approaches are necessary for navigating the multi-objective trade-offs common in medical engineering applications, such as mechanical and biological functionality in regenerative medicine [ 1 , 5 ]. Solving multi-objective problems requires weighing the importance of different variables for tuning a design for higher or lower performance in different situations. Such trade-offs are also inherent to navigate in 3D printing processes between speed, time, and mechanical performance, which are affected by fabrication artifacts. Finding high-value solutions in complex design space searches remains a challenge for 3D printing applications that could benefit from computational design approaches with improved navigation of search spaces [ 153 ]. Computational approaches could also aid in predication of part performance which is necessary for design search evaluations. For instance, the construction of multi-material parts has opened new possibilities in combining materials to create entirely new systems that operate differently than their individual components, and may exhibit advantageous emergent behaviors. Design customization on a per-print basis opens new doors for personalized medicine, but again, there is a need for new computational approaches for automatically tuning designs to a patient’s specified needs [ 154 ]. Future advances in design are necessary to aid nonlinear and integrated decision making across materials and processes for specified applications, where there is much room to explore new methods for fully leveraging the capabilities 3D polymer printing.

7. Conclusions

A review of polymer 3D printing for medical applications was conducted, and highlighted how material, process, and design decisions influence application performance, therefore necessitating designers to carefully consider all factors when configuring parts. Recent research has demonstrated a diversity of polymer materials with varied properties according to 3D printing processing parameters. Design strategies enable the directed placement of materials to achieve improved performance with configurations such as architected or multi-material structures. Because of the complexities involved in considering all factors that influence application performance, it is recommended that researchers conduct further experiments considering the interactions of materials, processes, and design strategies, while developing new methodologies to handle decision making and configuration for applications. Overall, advances in 3D polymer printing have demonstrated many successes for implemented designs, with a need for continued research to fully leverage the technology for wide-ranging applications in engineering and medicine.

Author Contributions

Conceptualization, A.M.E.A., N.R.K., N.K., and P.F.E.; resources, P.F.E.; writing—original draft preparation, A.M.E.A., N.R.K., N.K., and P.F.E.; writing—review and editing, A.M.E.A., N.R.K., N.K., and P.F.E.; supervision, P.F.E.; project administration, P.F.E. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Conflicts of interest.

The authors declare no conflict of interest.

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3D-printing techniques in a medical setting: a systematic literature review

Affiliations.

• 1 Department of Public Health, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium. [email protected].
• 2 Ghent University Hospital, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium.
• 3 Departement of Economics & Business Administration, Ghent University, Tweekerkenstraat 2, 9000, Ghent, Belgium.
• 4 Department of Public Health, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium.
• PMID: 27769304
• PMCID: PMC5073919
• DOI: 10.1186/s12938-016-0236-4

Background: Three-dimensional (3D) printing has numerous applications and has gained much interest in the medical world. The constantly improving quality of 3D-printing applications has contributed to their increased use on patients. This paper summarizes the literature on surgical 3D-printing applications used on patients, with a focus on reported clinical and economic outcomes.

Methods: Three major literature databases were screened for case series (more than three cases described in the same study) and trials of surgical applications of 3D printing in humans.

Results: 227 surgical papers were analyzed and summarized using an evidence table. The papers described the use of 3D printing for surgical guides, anatomical models, and custom implants. 3D printing is used in multiple surgical domains, such as orthopedics, maxillofacial surgery, cranial surgery, and spinal surgery. In general, the advantages of 3D-printed parts are said to include reduced surgical time, improved medical outcome, and decreased radiation exposure. The costs of printing and additional scans generally increase the overall cost of the procedure.

Conclusion: 3D printing is well integrated in surgical practice and research. Applications vary from anatomical models mainly intended for surgical planning to surgical guides and implants. Our research suggests that there are several advantages to 3D-printed applications, but that further research is needed to determine whether the increased intervention costs can be balanced with the observable advantages of this new technology. There is a need for a formal cost-effectiveness analysis.

Keywords: 3D printing; Additive manufacturing; Anatomic model; Customized; Innovation; Patient specific; Review; Surgery.

Publication types

• Systematic Review
• Models, Anatomic
• Printing, Three-Dimensional*
• Prostheses and Implants
• Surgical Procedures, Operative / methods*

3D bioprinting: current status and trends—a guide to the literature and industrial practice

• Open access
• Published: 02 December 2021
• volume  5 ,  pages 14–42 ( 2022 )

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• Silvia Santoni 1 , 2 ,
• Simone G. Gugliandolo 1 , 2 ,
• Mattia Sponchioni   ORCID: orcid.org/0000-0002-8130-6495 2 ,
• Davide Moscatelli 2 &
• Bianca M. Colosimo 1  

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The multidisciplinary research field of bioprinting combines additive manufacturing, biology and material sciences to create bioconstructs with three-dimensional architectures mimicking natural living tissues. The high interest in the possibility of reproducing biological tissues and organs is further boosted by the ever-increasing need for personalized medicine, thus allowing bioprinting to establish itself in the field of biomedical research, and attracting extensive research efforts from companies, universities, and research institutes alike. In this context, this paper proposes a scientometric analysis and critical review of the current literature and the industrial landscape of bioprinting to provide a clear overview of its fast-changing and complex position. The scientific literature and patenting results for 2000–2020 are reviewed and critically analyzed by retrieving 9314 scientific papers and 309 international patents in order to draw a picture of the scientific and industrial landscape in terms of top research countries, institutions, journals, authors and topics, and identifying the technology hubs worldwide. This review paper thus offers a guide to researchers interested in this field or to those who simply want to understand the emerging trends in additive manufacturing and 3D bioprinting.

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Introduction

Bioprinting is a collection of additive manufacturing (AM) technologies, whose aim is to fabricate parts imitating real tissue and organ functionalities by combining both living and non-living materials in a specific three-dimensional (3D) spatial organization structure. As in traditional 3D printing or AM, the target is achieved through the use of computer-aided design (CAD) that represents the fundamental configuration of the target tissue or organ, in order to produce bioengineered structures that have various applications in regenerative medicine, tissue engineering, reconstructive surgery, drug discovery, pharmacokinetics, medical and basic cell-biology research [ 1 ]. Compared to traditional 3D printing or AM processes, bioprinting brings a main innovative feature, namely the printing of living cells within a specific medium called bioink, which adds many different challenges, such as how to avoid the deterioration of living cells while printing constructs that have a 3D volumetric shape similar to the ones of natural tissues and organs.

In light of the application of such manifolds and the growing interest towards personalized medicine, bioprinting methods have attracted increasing attention in recent years from both academia and industry, which has translated into extensive research efforts. During the last decade, many novel procedures and technologies related to biomanufacturing have emerged, ranging from dedicated 3D bioprinters [ 2 ] to specific “raw biomaterials” named bioinks [ 3 , 4 ].

A bioprinter is a 3D printer that realizes biological tissue constructs by the layerwise deposition of living cells. To achieve this aim, bioprinters generally use bioinks, which are soft biomaterials loaded with living cells manipulated according to specific protocols to build biological constructs. The use of secondary dissolvable materials is an additional option to vertically support and protect cells during the printing process.

Although many bioprinting review papers focusing on describing techniques or bioink classifications have been published in recent years [ 3 , 5 , 6 , 7 ], a systematic and quantitative investigation of the actual landscape has not been performed, including the analysis of papers, patents and companies with the aim of highlighting the actual distribution of key players in academia and industry, as well as the main topics currently under study. To the best of our knowledge, the first and only scientometric review on 3D bioprinting cannot be considered up-to-date including the latest scientific innovations in this area, as it was published in 2017 [ 8 ] based on data retrieved from 2000 to mid-2016. In fact, two-thirds of the total publications related to bioprinting to date have been published since 2016.

Given the rapid growth of this special field, the present work is aimed at stimulating the interest of scientists and experts already involved in traditional 3D printing or AM by highlighting the emerging trends and the most recent advancements [ 1 , 9 , 10 , 11 , 12 ]. This review presents a rational roadmap to the scientific and patenting results produced to date, which can be especially useful for researchers new to the field, as they can quickly obtain the geographical distribution of laboratories and companies actively involved in 3D bioprinting combined with a critical analysis of their output in terms of publications, patents, new tools and manufacturing techniques.

The paper is organized as follows: the literature review results are presented and discussed in “ The academic research trends ” section with a detailed analysis of the most productive authors and active research networks worldwide. “ Market and patent landscape ” section describes the market and patent landscape to identify both emerging and established technology hubs. Finally, the main conclusions are drawn in “ Conclusions ” section.

The academic research trends

Trends in the relevant scientific literature: critical data analysis and classification of applications and trends.

Following previous scientometric studies and AM [ 8 , 12 ], we based our literature analysis considering all research and review papers published in scientific journals included in Scopus (Elsevier) and Web of Science (WoS) in the past 20 years (from 2000 to 2020). We also used SciVal ( https://www.scival.com/ ) as a supporting tool in our query. The latter was focused on bioprinting processes, materials and bioapplications according to the latest definition of bioprinting, and is a modified version of the one used by Rodríguez-Salvador et al. (details in the Supplementary Information). In order to better highlight the most recent trends, a detailed analysis was further performed with reference to scientific results published in the last four years, i.e., since 2016.

A total number of 13,111 papers (11,683 research articles and 2537 review papers) were initially collected using both the Scopus and WoS databases. An extensive cleaning and deduplication process was subsequently performed through EndNote (X9, Clarivate Analytics, Philadelphia, USA), leading to 9314 unique documents, consisting of 7574 research articles and 1740 review papers).

It is worth noting that 79% of these papers were published after 2014 and nearly 53% of total publications were published after 2017. Specifically, 61% (4620 out of 7574) of research articles and 74% (1288 out of 1740) of review papers have been published since 2016, showing an exponential growth of attention on this topic in the scientific literature. Figure  1 shows the total number of publications retrieved from Scopus for the last 20 years, where the steady rise during the past 10 years is clearly visible. This growing number of scientific papers led to a 143% increase in the number of review papers in a single year for 2016. Since then, due to the continuous evolution and rapid innovation in this field, a constant annual growth rate of (28 ± 9)% in review papers has been reported.

3D bioprinting publications by year: articles, blue; reviews, light blue

In order to select the most relevant venues for 3D bioprinting papers, SciVal ( https://www.scival.com/ ) was used to research on the topic T.8060 (Bioprinting; Printability; Tissue Engineering) together with InCites Journal Citation Reports to include information on Impact Factor, Article Citation Median and Review Citation Median focusing on 2018 and 2019 (details are also given in Table S1 of the Supplementary Information).

The number of papers published (usually referred to as ‘scholarly output’ Footnote 1 ) in the past five years was specifically used to select the twenty most productive journals in the bioprinting field. Figure  2 presents the main results of this ranking. As clearly seen in the figure, Biofabrication (with 319 publications, namely 297 articles and 22 review papers), Biomaterials (with 184 publications, namely 166 articles and 18 review papers), and Acta Biomaterialia (with 162 publications, consisting of 124 articles and 38 review papers) are the most prolific journals in this field. Moreover, the percentage of publications focusing on bioprinting with respect to the overall number of papers from 2000 to 2020 was used as an additional indicator of the level of attention to this topic (data retrieved from Scopus), and are shown as dots in Fig.  2 . As expected, Bioprinting (66%), Biofabrication (43%), International Journal of Bioprinting (42%), and Bio-Design and Manufacturing (26%) are the top-focalized journals. Most of these are young journals (founded in 2009, 2015, 2016, and 2018, respectively) focusing on this novel field, with impact factors (IF) revealing their age and their specific field of focus (IF values ranging from 4.10 for Bio-Design and Manufacturing to 8.21 of Biofabrication, compared with older and more generic journals such as Advanced Materials with IF equal to 27.4 Footnote 2 ).

The top twenty journals focusing on 3D bioprinting (SciVal-Scopus). The bars represent the number of publications (blue: articles, light blue: reviews) retrieved from Scopus, while the yellow dots represent the percentage of publications focusing on 3D bioprinting with regards to the total number of publications. The examined time interval is 2000–2020

With regard to review papers, a different classification can be outlined depending on the specific 3D bioprinting technology each paper refers to [ 13 , 14 ]. As for traditional AM processes, different bioprinting techniques vary in the technique of layerwise deposition of biomaterial. Even if the bioprinting literature does not assume the proper terminology defined in the AM standards (ISO/ASTM 52900), AM technologies similar to the ones used for polymers are often adopted. The first class of technologies is based on nozzle-deposition [ 11 , 15 , 16 , 17 , 18 , 19 ], which can have different printing resolutions and speed depending on the precision of the bioprinting head, the nozzle diameter size and the droplet formation mechanism (Fig.  3 a). A second main class of technologies are optical-based, namely the vat photopolymerization (always referred to as stereolithography in the literature on bioprinting [ 11 , 20 , 21 ]) both in its traditional setting and the two-photon polymerization version.

a Different procedures of 3D bioprinting, adapted from Derakhshanfar et al. and Loai et al. [ 22 , 23 ]. b Number of publications for each bioprinting technique (extrusion, stereolithography, laser-assisted and inkjet) for publication years 2000 to 2020; inset: 5-year publication trend for 2016–2020

Figure  3 b shows that extrusion-based bioprinting is the most studied approach in the literature, potentially because it is the most affordable solution for an entry-level bioprinter, and the least expensive technology that allows the use of a wide range of printable biomaterials [ 2 ]. The second and third most widespread techniques are vat photopolymerization and inkjet bioprinting. The former is characterized by many benefits, i.e., higher resolution, a wide variety of bioink viscosities and higher cell density [ 24 , 25 ]. Eventually, thanks to the drop-on-demand (DOD) patterning method available in most bioprinters, jetting is often used for printing smaller features.

The extrusion-based technique is rapidly becoming popular likely because of the great number of entry-level bioprinters that have entered the market in recent years. Meanwhile, vat photopolymerization 3D bioprinting is emerging as a prominent bioprinting method for complex tissues.

Bioprinting research landscape: main applications and emerging topics

The main utilities of 3D bioprinting are in basic medical/cell biology research, the production of pathology models, mini-tissue production for drug screening, and the field of regenerative medicine for the future replacement of tissues and organs [ 5 ]. Within this framework, the ideal workflow of bioprinting should start from retrieving patient-specific cells through biopsy, designing the morphology of the organ or tissue to be replaced, and going back to the patient at the end for the transplantation of a functional organ [ 26 , 27 , 28 , 29 , 30 , 31 ]. To the best of our knowledge, this ideal workflow cannot be yet completed from end to end, as different challenges [ 1 , 32 ] need to be overcome. Among the most important ones, vascularization and multi-material printing are the most relevant. Vascularization consists of printing tiny vessels and capillaries that are specifically designed to enable the survival of living cells by the delivery of nutrients and oxygen. Multiple materials are needed to allow different types of cells and hydrogels to be combined in the 3D structure, as it occurs in real biological tissues.

Considering the long-term goals and driving factors, research on 3D bioprinting is now progressing in three major areas:

Application-driven research focusing on specific utilities of 3D bioprinting, i.e., distinct tissues, pathology models or organ-on-a-chip for drug discovery.

Biomaterials research to develop novel bioink formulations that improve printability or support tissue differentiation and maturation, and allow the study of cells to be bioprinted in the construct.

Process-driven research focusing on the printing technology to improve the resolution and accuracy of 3D bioprinting while avoiding cell damage, support the design of complex shapes, reduce printing time and costs, and allow specific functionalities, i.e., multi-material printing.

In order to highlight the main trends in the literature, we clustered papers published since 2000 based on text analytics keywords. The number of articles related to each topic is shown together with its evolution over time in Fig.  4 .

Trends of publication topics on 3D bioprinting over the years. The number of publications relative to each topic are shown over time. The graph was created by counting at most one keyword in each topic class for each publication while having an average of two topics of interest in each publication

A considerable number of publications, especially review papers, are focused at the fundamental aspects of 3D bioprinting, and are included within the class of process-driven papers. For instance, a basic theme such as biomimicry shows steady growth from 2010, while there are newer ideas, including four-dimensional (4D) bioprinting that first appeared in 2016 and is already the subject of 28 papers [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Some publications show the bioprinting workflow [ 27 , 28 , 29 ] and areas [ 42 ], while the ethical aspects of bioprinting are still relatively underrepresented [ 43 ].

Regarding the applications of 3D bioprinting, about 40% of all publications refer to a specific tissue or organ starting with their title (as shown in Table 1 and the Supplementary Information). Many review papers are directed at the bone, cartilage (in particular, articular cartilage), vascularized tissue, cardiac tissues, liver, neural tissue, skin, pancreas, cornea, kidney and muscle, where the first classes mentioned are also the most frequently studied ones (see Fig.  5 ). On the other hand, some emerging topics have received increased attention in the last few years, such as dental tissue, nerve regeneration, lung, intestine, thyroid gland [ 44 ], urethra [ 45 ], and encapsulated T-cells [ 46 ]. This trend might continue in the near future.

Catalogue of all publications based on the automatic assignment of keywords extracted from the titles relative to the tissues and organs (others: articulation, nerve regeneration, kidney, adipose tissue, lung, dental, trachea, ear, pancreas, cornea, aortic valve, esophagus, retina, neural tissue, thyroid gland, urethra, intestine, eye, T-cells). The sum is not equivalent to the total number of publications, since each paper can focus on more than one tissue

Among other applications, graft and implants, pathology models, and organs-on-a-chip are also addressed, with a relative role (i.e., percentage of reviews over the total number of publications) showing an upward trend for the past 10 years. In this area, we can observe studies on traditional topics, such as bioglues, grafts and implants, but also new solutions including the BioPen (which is a handheld device invented by Wallace and co-workers [ 72 ] for printing cartilage in vivo) or the application of bioprinting to cryopreservation.

Since the beginning (the first papers date back to 2002), 3D bioprinting has also been subject to pathology models for in vitro studies of diseases. In particular, 3D-bioprinted cancer models have been described for breast cancer [ 115 , 116 , 117 , 118 , 119 , 120 , 121 ], mammary ductal carcinoma [ 115 ], appendiceal cancer [ 122 ], mesothelioma [ 123 ], glioblastoma and metastasis. Other types of diseases that have been modeled through bioprinting include epilepsy [ 124 ], diabetes [ 110 , 125 ], degenerative diseases, immune-enhanced organoids for immunotherapy screening [ 126 ], and wound healing [ 127 , 128 ]. In all these applications, 3D bioprinting has been utilized for drug discovery, drug screening, and pharmaceutical applications, especially after 2011. On the one hand, the production of pathological tissues and organs using cells from patients leads to a personalized approach on drug discovery [ 129 ]. On the other hand, the serial production of mini-tissues in a standardized manner can be highly useful for the high-throughput screening of large libraries of drugs already available on the market (drug screening [ 130 , 131 ] or novel drug discovery [ 132 ]). In the future, the main target is to 3D print patient-specific models using the patient’s own cells to test different chemotherapeutic drugs in vitro for selecting the most efficient patient-specific therapy. Translational medicine and the implications of 3D bioprinting in regenerative medicine, as well as the clinical translation of 3D bioprinted constructs [ 50 , 133 , 134 ], are certainly becoming hot topics in the near future.

Compared to other applications, publications on translational medicine occurred fairly lately (starting in 2009), adding up to 117 publications with more than 60% classified as review papers. In fact, the application of 3D-bioprinted tissues in medicine is still being implemented; to the best of our knowledge, no tissues or organs produced by 3D bioprinting have been implanted in vivo in real patients. However, the 3D printing of biomaterials [ 135 , 136 , 137 ] is increasingly common in medicine, especially for the production of bone and dental implants and grafts, but also in surgery for the production of patient-specific 3D models on which surgeons can train before the actual procedure.

Microfluidics and organs-on-a-chip are some of the latest areas in 3D bioprinting, and, even though the first occurrence dates back to 2004, most of the relevant publications have been published after 2010. At present, only about 100 publications refer to this topic by the title. Publications on organ-on-a-chip models focus either on modeling healthy or pathologic organs [ 138 ], where bioprinting can be useful for studying gene expression and cell differentiation in different healthy conditions by controlling the microfluidics and the microenvironment, or can be used to realize in vitro models for drug screening in pathology studies.

Concerning biomaterials, one of the most exciting field of research relates to bioinks, with about 25% of the whole number of publications on bioprinting focusing on the development of novel bioinks to obtain specific biological, mechanical, and chemical characteristics. This stream of research is fairly new, as research on bioinks was rather limited before the rise of 3D bioprinting. Nowadays, the number of reviews on bioprinting is growing together with the rising need of information to standardize tests on 3D cultures. On this subtopic, the literature focuses on imaging (73 publications), biological characterization (726 publications), resolution (49 publications) and printability (32 publications), with an increasing interest in rheology (21 publications) and structural integrity (9 articles).

Most of the recent papers on bioinks outline the need to find the best compromise between printability and specialization for the specific cell or tissue under study [ 139 , 140 ]. In fact, each cell type requires highly specific conditions in addition to a number of standard requirements (e.g., aqueous environment, sufficient oxygen and nutrient diffusion, appropriate pH, physiological osmolarity of key vitamins and minerals). For example, certain cell types require appropriate sites for attachment, specific substrate properties and space in order to proliferate and produce their extracellular matrix (ECM) [ 141 ]. Bioinks can be classified depending on their origin (natural or synthetic), the type of 3D printing process they can be used in (e.g., bioinks for material extrusion, jetting or photopolymerization differ in their rheological characteristics, shape fidelity and printability features) or the gelation kinetics: ionic, stereocomplex, thermal, photocrosslinking, enzymatic and click chemistry [ 142 ].

Overall, about 15% of all publications focus on innovative cell types in 3D bioprinting, such as stem cells, spheroids, and organoids. This rate is yet to increase mostly because innovative cell types are still under investigation in biology with the aim to overcome open challenges concerning differentiation and maturation. With reference to stem cells in 3D bioprinting [ 143 , 144 ], Skeldon et al. outlined that the main types of stem cells used in this context are mesenchymal stem cells, neural stem cells, and human induced Pluripotent Stem Cells (iPSCs) [ 143 ]. However, our search found that general multipotent human Adipose Stem Cells (hASCs), as well as nasal and bone marrow stem cells, have also been used. Spheroids have been used in 3D bioprinting since 2003, mostly as the living components of bioinks. Finally, organoids have become one of the latest cell sources used in 3D bioprinting since their first occurrence in 2017 [ 145 ].

Surprisingly, the characterization or development of new process technologies for 3D bioprinting has received rather limited attention in the literature. The rate of publications on this topic decreased from around 30% in 2010 to 15% in 2019. This can be mainly ascribed to the increasing focus on biology, medicine, or material science rather than engineering driving the increase of attention to bioprinting. Secondly, most of the processes used in this field are those borrowed from the traditional 3D printing of polymers with modifications to achieve the desired results. However, a lot of research is lacking, especially for most of the complex technologies. This is clearly visible in the literature, where most of the studied techniques are the laser-based ones (144 articles and 14 reviews) and stereolithography (83 articles and 18 reviews). Inkjet was introduced in 2006 and is among the oldest techniques, while extrusion 3D bioprinting first appeared in 2001, but expanded especially after 2015 with the entry of commercial bioprinters to the market.

Moreover, the application categories include printing techniques that simply exploit existing printing technologies and processes in innovative ways to meet the needs of a specific application (e.g., creating channels to form vascularized tissue). Such is the case of bioprinting in a suspension bath, primarily developed to create vascularized tissues. Among others, one of the most recent techniques is called freeform reversible embedding of suspended hydrogels (FRESH), which has now progressed to its second version and consists of extruding a bioink in a dissolvable suspension bath usually made of a gelatin microparticle slurry, which enables the 3D bioprinting of constructs with higher resolution and is useful for the production of vessels of very small diameters (5 to 10 µm) [ 146 ]. This technique has been used very recently for the 3D bioprinting of a full-size human heart [ 147 ]. An alternative utility of this type of technique is sacrificial writing in functional tissues (SWIFT), which enables the production of small vessels and vascularization through extrusion bioprinting directly inside a functional and vital tissue, which simultaneously acts as a suspension bath [ 63 ].

Moreover, a further highly innovative branch of applications is the magnetic levitation approach, introduced in 2020 by Mironov et al. (also affiliated to the company 3D Bioprinting Solutions [ 148 ]). However, the first experiments with magnetic-based bioprinters showed a limitation that the bioinks have to withstand the pull of Earth’s gravity. Regarding this aspect, space agencies like ESA or NASA are also investigating the idea of using microgravity to improve the 3D printing of soft human tissues, such as blood vessels and muscles. This means using a scaffold-free, nozzle-free and label-free approach (i.e., without magnetic nanoparticles). Enabling in-space bioprinting may not only help improve bioprinting research to face organ shortage on Earth but would also have repercussions in long-term/long-distance human space missions (including Moon and Mars programs). The increased risk of injuries in such distant missions impose the need to develop the ability to print replacement tissues or organs for astronauts in emergency situations. In this context, 3D bioprinting could be considered as a mission enabler for such kinds of projects (i.e., space exploration and planet colonization) [ 149 ].

Worldwide distribution of the most prolific academic institutions

In order to highlight countries and institutions currently involved in 3D bioprinting research, the geographical distribution of affiliations declared in the publications were analyzed. A preliminary analysis was performed on the aggregated data retrieved from SciVal. The United States (USA), China, South Korea, Germany, United Kingdom (UK), and Canada scored as the most relevant countries where research on 3D bioprinting is currently ongoing. Similar results were obtained by ranking the countries depending on the authors’ affiliations (see Fig.  6 a Footnote 3 for further details). As seen in Fig.  6 b, the US has an obvious leading role in terms of absolute performance (number of authors and institutions involved in bioprinting research), which shows a more diffused attention to this topic (with an average of 4.6 top authors in each of the leading institutions). Meanwhile, China has a second leading position but is characterized by a more focused profile, where only a handful of institutions are currently hosting the most prolific authors on 3D bioprinting (with 7.5 authors on average in each of the top institutions).

a Geographic localization of the current affiliation of the 100 most relevant authors (blue), and the most relevant affiliations (green) according to SciVal based on the Scholarly output. The ten most relevant universities are highlighted. The interactive map can be viewed at https://ggle.io/3kuZ . Map data ©2021 Google. b Number of the most prolific universities (retrieved by considering the affiliations in papers) and top authors per country. The number of the most relevant authors, in blue, and the number of the most relevant institutions per country, in green, were retrieved from SciVal on the topic T.8060 (Bioprinting; Printability; Tissue Engineering) . The countries are listed following the SciVal ranking based on the Scholarly output. China, South Korea, and Germany have the highest number of authors per affiliation. The fraction of authors over the number of institutions per country is represented in yellow, and the data are shown on the secondary y axis on the right

In Table 2 , the number in the parentheses after the research institute refers to the relative position of the institution/author in the worldwide ranking obtained by considering the number of published products (called ‘scholarly output’ in SciVal). In particular, products are associated to the institution depending on the affiliation of the authors of each product.

The table lists the top ten affiliations; it can be observed that the University of California at San Diego (1) and Harvard University (2) in the USA, and Nanyang Technological University (3) in Singapore are the three leading institutions in this field (see also Table S2 for a complete list of top affiliations and authors per country). A similar geographical distribution is shown for the most prolific authors (shown in blue in Fig.  6 b).

A more complete analysis of the top-leading laboratories and scientists is presented in Table 3 , with specific attention to the investigated topics. For the most inclusive analysis possible, these authors were selected as the 20 researchers with the highest scholarly output and/or citation count within the topic of 3D bioprinting according to SciVal. Moreover, the network of collaborations between universities defined by considering co-authorships is shown in Fig.  7 , from which it can be inferred that, despite global collaborations, the highest number of publications in collaborations are also geographically clustered. The clusters identified from this graph are also discussed in Table 4 .

Network graph showing collaborations between the most prolific authors; the authors’ names and relative affiliations are presented in color and black, respectively. The size of the node (circle) is directly proportional to the number of publications on 3D bioprinting retrieved from that author, while the color indicates the country of affiliation. The links between the nodes denote the number of collaborations (only collaborations on at least 10 publications are shown); the thickness of a link is proportional to the number of articles produced in collaboration between the two authors. Twelve clusters of collaborations can be identified from this graph, in which 5 are prominent in terms of the number of publications of authors and the number of collaborations

Within the US, three clusters of collaborations can be recognized. The most relevant group in the USA per number of publications can be referred to as the “Harvard cluster ” in which a strong collaboration between PIs affiliated to Harvard can be seen; the PIs involved are Khademhosseini, Ali, whose current first affiliation is Terasaki Institute for Biomedical Innovation, and Zhang, Yu Shrike, who is currently affiliated to Harvard Medical School. Considering authors’ multiple affiliations, this cluster also has a connection with Massachusetts Institute of Technology (6). Within this cluster, vascularization and heart [ 75 , 150 , 151 , 152 , 153 ] are the types of tissue attracting the greatest interest. In the US, another group of collaborations can be identified as the “ Wake Forest cluster ”, in which a network of connections can be recognized within the university with the affiliations of Atala, Anthony, Yoo, James, and Lee, Sangjin. Within this cluster, the focus is mainly on process [ 154 ], cartilage [ 155 ] and articulations [ 156 ].

A further research facility worth mentioning is the UC San Diego (1), which is the leading university in the world on 3D bioprinting, to which Chen, Shaochen is affiliated. Publications by this university are mainly focused on the optimization of the bioprinting process, particularly inkjet [ 157 , 158 , 159 , 160 ], and the evaluation of printability [ 161 , 162 ]; regarding the type of tissues, the recurrent topic is the creation of tubular structures and vasculature [ 163 ].

Within Asia, China is ranked second in terms of the number of publications (1036 papers), with leading institutions such as Zhejiang University (5), Tsinghua University (8) and the Chinese Academy of Sciences (9). Notably, while the USA has mainly academic players, among the 14 top institutions in China, two are government-run and one is a medical institution (see Fig. S1 in the Supplementary Information for further details). Interestingly, most of the collaborations in Asia occur within universities.

Within Zhejiang University (5), a strong collaboration can be noticed between Fu, Jianzhong, He, Yong, and Gao, Qing, with the main focus of publications on vascularization [ 164 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 183 ]. Other universities worth mentioning are Tsinghua University and the Ministry of Education, where Sun, Wei and Li, Xinda are the most prolific authors, respectively. The focus of these collaborations is on topics such as the inkjet process [ 184 , 185 ], biomaterials [ 186 ], with targeted efforts on tumor model preparation [ 187 ], especially regarding glioma [ 188 ] and lung cancer [ 189 ], the use of stem cells [ 190 ], and the formation of vasculature [ 191 ].

South Korea is ranked third in terms of published products (scholarly output from SciVal). The main academic institutions here are Pohang University of Science and Technology (7), Konkuk University (15), and Sungkyunkwan University (14). The Pohang University of Science and Technology (7) can be considered as the center of a relative cluster to which the Korea Polytechnic University also belongs. To the first affiliation, Cho, Dongwoo and Jang, Jinah are active and mainly focused on the liver [ 192 , 193 ], cardiac repair [ 194 , 195 ], cartilage [ 196 ], vascularization [ 197 , 198 ], and cornea [ 111 , 199 ].

Within Asia, further notable institutions are located in Singapore (6), which is globally ranked the sixth in terms of number of publications, with the main participating institutions of Agency for Science, Technology and Research (40), to which Naing, May Win is affiliated, and the Singapore University of Technology and Design, to which Chua, Chee Kai is affiliated. Moreover, the most prolific institution in Russia is the Sechenov First Moscow State Medical University, to which Mironov, Vladimir A. is affiliated.

In Europe, Germany (4) and the UK (5) are the two leading countries in terms of publications, number of top authors and top institutions. However, the most productive institution on bioprinting in Europe is Utrecht University in the Netherlands (10). Four clusters of collaborations can be identified within Europe, one being the “ Utrecht University cluster ”, which primarily links Malda, Jos and Levato, Riccardo from Utrecht University (10), and Groll, Jürgen from University of Würzburg in Germany (4), with a main focus on the general aspects of 3D bioprinting [ 14 , 31 , 200 , 201 , 202 ]. Two additional clusters of collaborations can be identified in Germany within the Technische Universität Dresden with researchers Lode, Anja and Ahlfeld, Tilman, and Friedrich-Alexander University Erlangen-Nürnberg to which Boccaccini, Aldo R. and Detsch, Rainer are affiliated. In addition, a cluster of collaboration can be identified in Poland with a collaboration between the Warsaw University of Technology (Święszkowski, Wojciech) and the Polish Academy of Sciences (Costantini, Marco).

Finally, it is worth noting that some leading universities are also located in Oceania; the University of Wollongong in Australia, to which Wallace, Gordon G. and Yue, Zhilian are affiliated, and the University of Otago in New Zealand, to which Woodfield, T. B.F. and Lim, K. S. are affiliated.

Market and patent landscape

In recent years, interest in 3D bioprinting has been gathering momentum not only in academia, but also in the industry. Between 2014 and 2015, numerous 3D bioprinting companies have entered the market, and new start-ups, spin-offs and subsidiaries continue to emerge. Bioprinting could become a new standard for the biofabrication of tissues in the field of regenerative medicine; many bioprinter manufacturers have started to commercialize their proposals and services in research or other professional fields. Most of these companies sell materials (bioinks and cells), bioprinters and consulting services.

According to the latest market research by Mordor Intelligence [ 260 ], the global bioprinting industry was valued at USD 586.13 million in 2019 and is expected to reach USD 1,949.94 million by 2025, which is equivalent to a compound annual growth rate (CAGR) of 21.91% for the period of 2020–2025 [ 261 ]. These values were confirmed by another report, in which the value of 3D bioprinting market was projected to reach USD 1,647.4 million by 2024 at a CAGR of 20.4% for 2019–2024.

The growth of the 3D bioprinting industry, which is mainly driven by technological improvements on biomaterials and 3D bioprinters, has pushed business players to develop and enhance their existing manufacturing and distribution capabilities.

To review and analyze the companies and start-ups currently on the market, we used commercial magazines, newsletters and specialized blogs to retrieve 70 legally claimed bioprinting companies (latest update in July 2020). The analysis excluded 3D printing or biotechnology companies which announced their entrance into the market with no actual 3D bioprinting-related commercial products or services offered. The list of these companies, together with the available basic information regarding their business and their bioprinter models are reported in Table S3.

Based on the analysis, the business models of such companies could be classified as follows: (a) those selling commercial bioprinters and/or bioinks (63% of the whole market), (b) those providing bioprinting services (such as CAD modelling, specific tissue or cell culture constructs, scaffolds, grafts, or only consulting) with their own proprietary technology or commercially unavailable bioprinters (37% of the industry) and/or starting custom tissue partnership with clients (usually cosmetics or pharmaceutics industries) that have specific requests, as well as granting technology access partnerships (Table 5 ).

Around 80% of the market is composed of established companies, while 20% are start-ups with strong economic growth, mainly stemming from university spin-offs.

Table 6 reports the bioprinter market composition classified by technique, based only on the available information from manufacturer’s websites. Once again, it is possible to see that extrusion-based models are the most widespread ones, as their popularity is guaranteed by the lower cost and ease of use. Inkjet-based bioprinters consist the second most common technology. Nowadays, the inkjet technology is included in most of the extrusion-based bioprinters commercially available as an additional printing head. Despite the fact that stereolithography was the first technology in AM, stereolithography-based bioprinters are a new addition to the bioprinting industry, some of which only appeared at the time of writing of this paper or have yet to be announced. Laser-assisted bioprinters are among the most expensive bioprinters, which are usually part of more sophisticated systems. These are among devices capable of reaching the highest resolutions on the market. Only two-photon stereolithography has even better resolution, but it is not always categorized as a pure bioprinter, as this system is mainly useful for printing scaffolds for cells to attach to rather than printing cells and using bioink at the same time.

Based on the previous analysis, the industry is obviously growing at a fast rate not only in terms of quantity, but also in terms of diversification of the technologies developed and offered. Even though there are some polarizing countries, the companies that develop and commercialize bioprinting technologies are relatively dispersed across nearly all continents (Fig.  8 a).

a Worldwide distribution of 3D bioprinting companies. The interactive map can be viewed at https://ggle.io/3kuZ . Map data ©2021 Google. b 3D bioprinting market composition by continent

Mapping the companies making up this industry is essential to find potential technology hubs.

Considering single countries, the retrieved data suggests that USA remains the most significant player with 39% of all companies, exceeding all the other countries by one order of magnitude, whose percentages vary between 7 and 1%. In terms of continents, apart from the 40% share of North America, consisting basically of USA and Canada, Europe harbors 36% of all companies, with countries like Germany, UK and France representing nearly half of all European companies. The continents that follow are Asia (14%), Latin America (8%) and Oceania (1%) (Fig.  8 b).

As far as we are concerned, there is a multitude of university start-ups, especially in China and in Latin America, that prefer to use their own custom-designed bioprinting technologies.

Emerging technological trends

The fact that several 3D bioprinting companies across the globe currently manufacture commercially available 3D bioprinters is a clear indication that the field of AM and the bioengineering industry are evolving at a rapid pace. Along with the number of companies, the abundance of technological innovations associated with bioprinters and bioinks is also growing rapidly. In fact, the main leading bioprinting companies are trying to break into the market with increasingly peculiar technologies.

Most of the companies try to produce all-in-one extrusion-based bioprinting platforms with support for multi-materials (viscous pastes, gels and hydrogels, ABS/PLA and other filaments or polymer powders, liquids, ceramics and foods), multi-tools (laser system for ultra-high-precision cutting and engraving, CNC milling machine, photo-crosslinking UV LED, microscope, HD cameras for monitoring, autocalibration tools, 3D electronics printer, built-in incubator) and custom-made software (e.g. AI powered automatic organ and tissue segmentation software), often available in different versions according to customer requirements [ 153 , 220 , 244 , 245 , 262 , 263 , 264 , 265 ].

This panorama also includes firms that invest their resources in developing more refined solutions that aim to solve specific problems. A possible starting trend is to develop methods capable of using tissue spheroids and managing them, for example, through magnetic bioprinting such as the Organ.Aut, a magnetic bioprinter from the Russian company 3D Bioprinting Solutions [ 266 ], also delivered to the ISS on board the Soyuz MS-11 spacecraft. Furthermore, the Japanese company Cyfuse Biomedical [ 267 ] developed a platform that allows to create scaffold-free tissues using the Kenzan bioprinting method to manipulate spheroids. In this method, the production of 3D constructs is achieved by placing cellular spheroids in a temporary array of needles through a cell-dispensing robotic mechanism. On the other hand, there are companies, such as the Germany-based Cellbricks, that prefer to produce complex 3D-printed cell culture structures with a proprietary non-commercial stereolithography-based bioprinting platform [ 268 ].

Moreover, some enterprises try to propose bioprinters with more degrees of freedom to increase system flexibility and the range of printable features, like the American company Advanced Solutions [ 269 ], which patented a six-axis robotic extrusion-based bioprinter arm capable of loading up to ten independent biomaterials during a single print run. Other companies decided to focus on unusual features of their 3D bioprinters, such as the Rollovesselar™ module of the Chinese company Revotek for printing scaffold-free 3D cylindrical structures with a proprietary bio-ink to create vessels. This company claimed to have successfully replaced a short segment of the abdominal artery in 30 rhesus monkeys [ 270 ].

The bioprinting industry is not only driven by extrusion-based platforms. Other technologies to achieve the single cell deposition accuracy are under development, such as the Image Based Single Cell Isolation (IBSCI) developed by the French company Cellenion [ 271 ], which is a high-resolution-based technology consisting of automated image acquisition, processing and advanced algorithms to automatically isolate single cells from a cell suspension. Another French company, Poietis [ 272 ] focuses on laser-assisted bioprinting combined with extrusion-based and inkjet technologies supported via a proprietary PIA™ software to reconstitute the 3D representation of an entire tissue, layer after layer. Yet other companies, such as the Canada-based Aspect Biosystems, attempt to achieve improved accuracy in the development of microfluidic platforms equipped with an on-printhead crosslinking system that is able to print bioinks with a coaxial shell.

Some new business entities aim to increase their market share by widening the offer, producing affordable systems and collaborating with other entities. This is the case of CELLINK [ 273 ] that provides a wide range of solutions, both in terms of affordable bioprinters (extrusion-based and DLP-based) and various specific bioinks. In connection with Prellis Biologics, they have just released one of the first systems using two-photons stereolithography to the market, named the Holograph X™, with a special solution to increase the 3D printing speed by using a parallel set of photons, i.e., a multiphoton technology, in order to simultaneously cure millions of points in the bioink, and in turn achieve bioprinting speeds of up to 250,000 voxels per second.

Pioneering bioprinting companies like Organovo [ 274 ] instead prefer to provide services or products (like liver and kidney tissue models histologically and functionally similar to the native ones [ 241 , 275 ]) along with their proprietary technology.

It is also worth mentioning BIOLIFE4D, an upcoming biotech firm founded in 2015, with headquarters in Illinois (USA). The company is dedicated to produce a patient-specific, fully functioning heart through 3D bioprinting and with a patient’s own cells. In 2018, BIOLIFE4D successfully constructed a 3D-bioprinted vascularized and contractile cardiac patch made of iPSC. In 2019, they claimed that their next milestone would be to produce a human mini-heart, which would constitute the 3D-bioprinted mini version of a full-sized heart [ 276 ].

Evolution of patent trends

The industrial interest toward 3D bioprinting can be quantified in terms of number of deposited patents, which reflects the propensity of a company to protect its ideas and solutions. In this work, the Espacenet website [ 277 ] was used to identify the patents submitted in this field.

A new version of the global query matching the syntax and other specifications of this different database was made. A patent search was conducted in July 2020, and a total of 309 patent abstracts were found since the year of 2000. The abstracts of all patent records were carefully reviewed and grouped into the following categories: “bioprinting method”, “bioink”, “scaffold”, “bioprinter technology”, and “marginal involvement of 3D printing”.

At first glance, it is apparent that the number of patents published shows exponential growth, just as the number of scientific publications. Two-thirds of all patents found were published in the last 3 years (Fig.  9 a). This further confirms the growing number of companies and researchers entering this market.

a 3D bioprinting patent publication by year; b 3D bioprinting patent landscape composition per continent

Despite the fact that, as the previous analysis has highlighted, nearly all of the main bioprinting-related companies are based in the USA and Europe, more than two-third of the patents originate from Asia (Fig.  9 b). It is important to underline that most of these patents were published recently, which is a good sign that Asian companies are expected to soon break into the market. Among the Asian countries, China is leading the field of 3D bioprinting with 58% of all patents published so far (against 19% of USA), followed by South Korea (14%).

Another interesting aspect concerns the topic of patents (Table 7 ). Nearly half of them are about new bioprinting methods for specific functions (bone, vascular, trachea graft), for describing novel 3D bioprinting techniques, or to patent new bioprinter technologies. One-third is instead relative to biomaterials: novel bioink formulations rather than specific applications for specific bioink.

Intriguingly, patents regarding scaffold production or bioprinter technologies were more common in the early years, while those concerning bioinks or specific applications became more prevalent later. This is probably an indication that current technologies have been somewhat established, and new solutions in this area can more easily concern new material developments for organ- or tissue-specific customization.

Figure  10 demonstrates that over two-thirds of the considered patents came from universities or unaffiliated scientists. It is clear that, in recent years, the number of academic applicants (i.e., universities, hospitals and research centers) is growing much faster than those coming from the industrial sector, whose number stays fairly constant. A more in-depth analysis of the patent origin (Figs. 10 , 11 ) indicates that about 56% of those in the academic field and 61% of those in the industry come from China, which means that research output on bioprinting in this country is still booming. It is thus possible to justify the huge discrepancy between the high number of Chinese patents and the low number of Chinese companies. The next few years will probably see the birth of a growing number of Chinese companies focused on bioprinting.

Distribution of patent applicants by year since 2011: Universities/Hospitals/Research centers, blue; Companies/Corporations, orange; Scientists with no affiliation, grey

Country distribution of patent applicants by year: a Universities/Hospitals/Research centers patent; b Companies/Corporations patent. Top countries: China, blue; USA, orange; South Korea, grey

Conclusions

The field of 3D bioprinting, which represents a novel area within AM technologies, shows a great potential for future expansion. In the last few years, this discipline has received an impressive level of interest in the scientific literature, attracting many innovators and creating new exciting markets. All these signals outline that we are possibly observing the expansion of a long-term research direction. Instead of preparing an additional review paper, the aim of this study was to provide the reader with a comprehensive overview of the academic and industry landscape of 3D bioprinting, in order that unfamiliar researchers have a compass to venture into exciting emerging technologies, and experienced academics are provided with an updated snapshot of the current status of this fast-changing field.

In the first part, a scientometric review of the literature was provided, with an analysis of all of the impressive literature (almost 10,000 papers, with most of them published in the last few years) to highlight the globally most relevant applications and key actors in terms of laboratories and research networks.

In the second part, the associated companies and emerging technologies were described to highlight the upcoming innovations and the most relevant players that consider the technology for new market developments.

It was confirmed that both paper and patent publications exhibited exponential growth in this sector, with the USA leading the level of scientific output while China showing an impressive growth in the whole number of patents, which clearly highlights its possible future position as a leading country in the bioprinting industry.

Many open challenges highlighted in this study call for new technological solutions that can be possibly borrowed from traditional AM research. The enhancement of printing resolution and speed, as well as cost reduction are common challenges to be faced in the near future. Remarkably though, bioprinting has certain unique features, such as the requirement of avoiding the mistreatment of cells during printing, and taking multi-material printing as a key asset for future technological developments.

To achieve this aim, multidisciplinary research should combine engineering expertise in AM, biological knowledge on cell growth and differentiation, material science for biomaterial developments, and expertise in biomedicine and pharmaceutics to highlight and solve relevant research questions. With such a multidisciplinary approach, we might see a flourishing area that can have a relevant impact on successful future technologies aimed at the improvement of human wellbeing.

The Scholarly Output measures the number of research outputs [ 278 ].

IF data refer to 2019.

Data from SciVal, map created using Google MyMaps.

Abbreviations

Two-dimensional

Three-dimensional

Four-dimensional

Acrylonitrile butadiene styrene

Artificial intelligence

• Additive manufacturing

Computer-aided design

Compound annual growth rate

Computerized numerical control

Digital light processing

Drop-on-demand

Extracellular matrix

Gelatin methacryloyl

Human adipose stem cells

High-definition

Image-based single cell isolation

Impact factor

Induced pluripotent stem cell

International space station

Polycaprolactone

Polylactic acid

Poly (lactic-co-glycolic acid)

United Kingdom

United States

United States Dollar

Ultraviolet light emitting diode

Web of science

Ng WL, Chua CK, Shen YF (2019) Print me an organ! Why we are not there yet. Prog Polym Sci 97:101145. https://doi.org/10.1016/j.progpolymsci.2019.101145

Article   Google Scholar  

Choudhury D, Anand S, Naing MW (2018) The arrival of commercial bioprinters—towards 3D bioprinting revolution! Int J Bioprint 4(2). https://doi.org/10.18063/IJB.v4i2.139

Hölzl K, Lin S, Tytgat L et al (2016) Bioink properties before, during and after 3D bioprinting. Biofabrication 8(3):032002. https://doi.org/10.1088/1758-5090/8/3/032002

Hospodiuk M, Dey M, Sosnoski D et al (2017) The bioink: a comprehensive review on bioprintable materials. Biotechnol Adv 35(2):217–239. https://doi.org/10.1016/j.biotechadv.2016.12.006

Murphy SV, Atala A (2014) 3D bioprinting of tissues and organs. Nat Biotechnol 32(8):773–785. https://doi.org/10.1038/nbt.2958

Li J, Chen M, Fan X et al (2016) Recent advances in bioprinting techniques: approaches, applications and future prospects. J Transl Med 14(1):271. https://doi.org/10.1186/s12967-016-1028-0

Ozbolat IT, Moncal KK, Gudapati H (2017) Evaluation of bioprinter technologies. Addit Manuf 13:179–200. https://doi.org/10.1016/j.addma.2016.10.003

Rodríguez-Salvador M, Rio-Belver RM, Garechana-Anacabe G (2017) Scientometric and patentometric analyses to determine the knowledge landscape in innovative technologies: the case of 3D bioprinting. PLoS ONE 12(6). https://doi.org/10.1371/journal.pone.0180375

Agarwala S, Lee JM, Ng WL et al (2018) A novel 3D bioprinted flexible and biocompatible hydrogel bioelectronic platform. Biosens Bioelectron 102:365–371. https://doi.org/10.1016/j.bios.2017.11.039

Munoz-Abraham AS, Rodriguez-Davalos MI, Bertacco A et al (2016) 3D printing of organs for transplantation: Where are we and where are we heading? Curr Transpl Rep 3(1):93–99. https://doi.org/10.1007/s40472-016-0089-6

Antoshin AA, Churbanov SN, Minaev NV et al (2019) LIFT-bioprinting, is it worth it? Bioprinting 15(May):e00052. https://doi.org/10.1016/j.bprint.2019.e00052

Jin Y, Ji S, Li X et al (2017) A scientometric review of hotspots and emerging trends in additive manufacturing. J Manuf Technol Manag 28(1):18–38. https://doi.org/10.1108/JMTM-12-2015-0114

Ramos T, Moroni L (2020) Tissue engineering and regenerative medicine 2019: the role of biofabrication—a year in review. Tissue Eng Part C Methods 26(2):91–106. https://doi.org/10.1089/ten.tec.2019.0344

Moroni L, Boland T, Burdick JA et al (2018) Biofabrication: a guide to technology and terminology. Trends Biotechnol 36(4):384–402. https://doi.org/10.1016/j.tibtech.2017.10.015

Gao G, Kim BS, Jang J et al (2019) Recent strategies in extrusion-based three-dimensional cell printing toward organ biofabrication. ACS Biomater Sci Eng 5(3):1150–1169. https://doi.org/10.1021/acsbiomaterials.8b00691

Panwar A, Tan LP (2016) Current status of bioinks for micro-extrusion-based 3D bioprinting. Molecules 21(6). https://doi.org/10.3390/molecules21060685

Davoodi E, Sarikhani E, Montazerian H et al (2020) Extrusion and microfluidic-based bioprinting to fabricate biomimetic tissues and organs. Adv Mater Technol 5(8). https://doi.org/10.1002/admt.201901044

Boularaoui S, Al Hussein G, Khan KA et al (2020) An overview of extrusion-based bioprinting with a focus on induced shear stress and its effect on cell viability. Bioprinting 20:e00093. https://doi.org/10.1016/j.bprint.2020.e00093

Gudapati H, Dey M, Ozbolat I (2016) A comprehensive review on droplet-based bioprinting: past, present and future. Biomaterials 102:20–42. https://doi.org/10.1016/j.biomaterials.2016.06.012

Ng WL, Lee JM, Zhou M et al (2020) Vat polymerization-based bioprinting—process, materials, applications and regulatory challenges. Biofabrication 12(2). https://doi.org/10.1088/1758-5090/ab6034

Kumar H, Kim K (2020) Stereolithography 3D bioprinting. Methods Mol Biol 2140:93–108

Derakhshanfar S, Mbeleck R, Xu K et al (2018) 3D bioprinting for biomedical devices and tissue engineering: a review of recent trends and advances. Bioact Mater 3(2):144–156. https://doi.org/10.1016/j.bioactmat.2017.11.008

Loai S, Kingston BR, Wang Z et al (2019) Clinical perspectives on 3D bioprinting paradigms for regenerative medicine. Regen Med Front 1(1). https://doi.org/10.20900/rmf20190004

Zhang J, Xiao P (2018) 3D printing of photopolymers. Polym Chem 9(13):1530–1540. https://doi.org/10.1039/c8py00157j

Pawar AA, Saada G, Cooperstein I et al (2016) High-performance 3D printing of hydrogels by water-dispersible photoinitiator nanoparticles. Sci Adv 2(4):e1501381. https://doi.org/10.1126/sciadv.1501381

Datta P, Barui A, Wu Y et al (2018) Essential steps in bioprinting: from pre- to post-bioprinting. Biotechnol Adv 36(5):1481–1504. https://doi.org/10.1016/j.biotechadv.2018.06.003

Sun W, Starly B, Daly AC et al (2020) The bioprinting roadmap. Biofabrication 12(2):022002. https://doi.org/10.1088/1758-5090/ab5158

Kengla C, Renteria E, Wivell C et al (2017) Clinically relevant bioprinting workflow and imaging process for tissue construct design and validation. 3D Print Addit Manuf 4(4):239–247. https://doi.org/10.1089/3dp.2017.0075

Hunsberger J, Harrysson O, Shirwaiker R et al (2015) Manufacturing road map for tissue engineering and regenerative medicine technologies. Stem Cells Transl Med 4(2):130–135. https://doi.org/10.5966/sctm.2014-0254

Rezende RA, Selishchev SV, Kasyanov VA et al (2013) An organ biofabrication line: enabling technology for organ printing. Part I: from biocad to biofabricators of spheroids. Biomed Eng 47(3):116–120. https://doi.org/10.1007/s10527-013-9348-3

Levato R, Jungst T, Scheuring RG et al (2020) From shape to function: The next step in bioprinting. Adv Mater 32(12). https://doi.org/10.1002/adma.201906423

Gao G, Huang Y, Schilling AF et al (2018) Organ bioprinting: Are we there yet? Adv Healthc Mater 7(1). https://doi.org/10.1002/adhm.201701018

Miao S, Cui H, Nowicki M et al (2018) Stereolithographic 4D bioprinting of multiresponsive architectures for neural engineering. Adv Biosyst 2(9):9. https://doi.org/10.1002/adbi.201800101

An J, Chua CK, Mironov V (2016) A perspective on 4D bioprinting. Int J Bioprint 2(1):3–5. https://doi.org/10.18063/IJB.2016.01.003

Miao S, Zhu W, Castro NJ et al (2016) 4D printing smart biomedical scaffolds with novel soybean oil epoxidized acrylate. Sci Rep 6. https://doi.org/10.1038/srep27226

Lukin I, Musquiz S, Erezuma I et al (2019) Can 4D bioprinting revolutionize drug development? Expert Opin Drug Discov 14(10):953–956. https://doi.org/10.1080/17460441.2019.1636781

Gao B, Yang Q, Zhao X et al (2016) 4D bioprinting for biomedical applications. Trends Biotechnol 34(9):746–756. https://doi.org/10.1016/j.tibtech.2016.03.004

Esworthy TJ, Miao S, Lee SJ et al (2019) Advanced 4D-bioprinting technologies for brain tissue modeling and study. Int J Smart Nano Mater 10(3):177–204. https://doi.org/10.1080/19475411.2019.1631899

Li YC, Zhang YS, Akpek A et al (2017) 4D bioprinting: the next-generation technology for biofabrication enabled by stimuli-responsive materials. Biofabrication 9(1). https://doi.org/10.1088/1758-5090/9/1/012001

Yang GH, Yeo M, Koo YW et al (2019) 4D bioprinting: technological advances in biofabrication. Macromol Biosci 19(5). https://doi.org/10.1002/mabi.201800441

Castro NJ, Meinert C, Levett P et al (2017) Current developments in multifunctional smart materials for 3D/4D bioprinting. Curr Opin Biomed Eng 2:67–75. https://doi.org/10.1016/j.cobme.2017.04.002

Ozbolat IT, Peng W, Ozbolat V (2016) Application areas of 3D bioprinting. Drug Discov Today 21(8):1257–1271. https://doi.org/10.1016/j.drudis.2016.04.006

Vijayavenkataraman S (2016) A perspective on bioprinting ethics. Artif Organs 40(11):1033–1038. https://doi.org/10.1111/aor.12873

Bulanova EA, Koudan EV, Degosserie J et al (2017) Bioprinting of a functional vascularized mouse thyroid gland construct. Biofabrication 9(3):3. https://doi.org/10.1088/1758-5090/aa7fdd

Zhang K, Fu Q, Yoo J et al (2017) 3D bioprinting of urethra with PCL/PLCL blend and dual autologous cells in fibrin hydrogel: an in vitro evaluation of biomimetic mechanical property and cell growth environment. Acta Biomater 50:154–164. https://doi.org/10.1016/j.actbio.2016.12.008

Kim J, Hope CM, Gantumur N et al (2020) Encapsulation of Human Natural and Induced Regulatory T-Cells in IL-2 and CCL1 Supplemented Alginate-GelMA Hydrogel for 3D Bioprinting. Adv Funct Mater 30(15). https://doi.org/10.1002/adfm.202000544

Murphy SV, De Coppi P, Atala A (2019) Opportunities and challenges of translational 3D bioprinting. Nat Biomed Eng 4(4):370–380. https://doi.org/10.1038/s41551-019-0471-7

Matai I, Kaur G, Seyedsalehi A et al (2020) Progress in 3D bioprinting technology for tissue/organ regenerative engineering. Biomaterials 226. https://doi.org/10.1016/j.biomaterials.2019.119536

Singh M, Jonnalagadda S (2020) Advances in bioprinting using additive manufacturing. Eur J Pharm Sci 143:105167. https://doi.org/10.1016/j.ejps.2019.105167

Heinrich MA, Liu W, Jimenez A et al (2019) 3D bioprinting: from benches to translational applications. Small 15(23):1–47. https://doi.org/10.1002/smll.201805510

Tasnim N, De la Vega L, Anil Kumar S et al (2018) 3D bioprinting stem cell derived tissues. Cell Mol Bioeng 11(4):219–240. https://doi.org/10.1007/s12195-018-0530-2

Chameettachal S, Yeleswarapu S, Sasikumar S et al (2019) 3D bioprinting: recent trends and challenges. J Indian Inst Sci 99(3):375–403. https://doi.org/10.1007/s41745-019-00113-z

Shahabipour F, Ashammakhi N, Oskuee RK et al (2020) Key components of engineering vascularized 3-dimensional bioprinted bone constructs. Transl Res 216:57–76. https://doi.org/10.1016/j.trsl.2019.08.010

Nowicki M, Zhu W, Sarkar K et al (2020) 3D printing multiphasic osteochondral tissue constructs with nano to micro features via PCL based bioink. Bioprinting 17. https://doi.org/10.1016/j.bprint.2019.e00066

Dhawan A, Kennedy PM, Rizk EB et al (2019) Three-dimensional bioprinting for bone and cartilage restoration in orthopaedic surgery. J Am Acad Orthop Surg 27(5):E215–E226. https://doi.org/10.5435/JAAOS-D-17-00632

Ashammakhi N, Hasan A, Kaarela O et al (2019) Advancing Frontiers in bone bioprinting. Adv Healthcare Mater 8(7). https://doi.org/10.1002/adhm.201801048

Yun BG, Lee SHS-H, Jeon JH et al (2019) Accelerated bone regeneration via three-dimensional cell-printed constructs containing human nasal turbinate-derived stem cells as a clinically applicable therapy. ACS Biomater Sci Eng 5:6171–6185

Datta P, Dhawan A, Yu Y et al (2017) Bioprinting of osteochondral tissues: a perspective on current gaps and future trends. Int J Bioprint 3(2):109–120. https://doi.org/10.18063/IJB.2017.02.007

Yang J, Zhang YS, Yue K (2017) Cell-laden hydrogels for osteochondral and cartilage tissue engineering. Acta Biomater 57:1–25. https://doi.org/10.1016/j.actbio.2017.01.036

Chimene D, Miller L, Cross LM et al (2020) Nanoengineered osteoinductive bioink for 3D bioprinting bone tissue. ACS Appl Mater Interf 12(14):15976–15988. https://doi.org/10.1021/acsami.9b19037

Bendtsen ST, Quinnell SP, Wei M (2017) Development of a novel alginate-polyvinyl alcohol-hydroxyapatite hydrogel for 3D bioprinting bone tissue engineered scaffolds. J Biomed Mater Res Part A 105(5):1457–1468. https://doi.org/10.1002/jbm.a.36036

Xing F, Xiang Z, Rommens PM et al (2020) 3D bioprinting for vascularized tissue-engineered bone fabrication. Materials 13(10). https://doi.org/10.3390/ma13102278

Skylar-Scott MA, Uzel SGM, Nam LL et al (2019) Biomanufacturing of organ-specific tissues with high cellular density and embedded vascular channels. Sci Adv 5(9). https://doi.org/10.1126/sciadv.aaw2459

Sarker MD, Naghieh S, Sharma NK et al (2019) Bioprinting of vascularized tissue scaffolds: influence of biopolymer, cells, growth factors, and gene delivery. J Healthc Eng 2019. https://doi.org/10.1155/2019/9156921

Koduru SV, Leberfinger AN, Pasic D et al (2019) Cellular based strategies for microvascular engineering. Stem Cell Rev Rep 15(2):218–240. https://doi.org/10.1007/s12015-019-09877-4

Datta P, Ayan B, Ozbolat IT (2017) Bioprinting for vascular and vascularized tissue biofabrication. Acta Biomater 51:1–20. https://doi.org/10.1016/j.actbio.2017.01.035

Mouser VHM, Levato R, Mensinga A et al (2020) Bio-ink development for three-dimensional bioprinting of hetero-cellular cartilage constructs. Connect Tissue Res 61(2):137–151. https://doi.org/10.1080/03008207.2018.1553960

Yi HG, Choi YJ, Jung JW et al (2019) Three-dimensional printing of a patient-specific engineered nasal cartilage for augmentative rhinoplasty. J Tissue Eng 10. https://doi.org/10.1177/2041731418824797

Duchi S, Onofrillo C, O’Connell CD et al (2017) Handheld co-axial bioprinting: application to in situ surgical cartilage repair. Sci Rep 7(1):1. https://doi.org/10.1038/s41598-017-05699-x

Francis SL, Di Bella C, Wallace GG et al (2018) Cartilage tissue engineering using stem cells and bioprinting technology—barriers to clinical translation. Front Surg 5. https://doi.org/10.3389/fsurg.2018.00070

Di Bella C, Duchi S, O’Connell CD et al (2018) In situ handheld three-dimensional bioprinting for cartilage regeneration. J Tissue Eng Regen Med 12(3):611–621. https://doi.org/10.1002/term.2476

O’Connell CD, Di Bella C, Thompson F et al (2016) Development of the Biopen: a handheld device for surgical printing of adipose stem cells at a chondral wound site. Biofabrication 8(1). https://doi.org/10.1088/1758-5090/8/1/015019

Wang Z, Lee SJ, Cheng HJ et al (2018) 3D bioprinted functional and contractile cardiac tissue constructs. Acta Biomater 70:48–56. https://doi.org/10.1016/j.actbio.2018.02.007

Cui H, Miao S, Esworthy T et al (2018) 3D bioprinting for cardiovascular regeneration and pharmacology. Adv Drug Deliv Rev 132:252–269. https://doi.org/10.1016/j.addr.2018.07.014

Zhang YS, Arneri A, Bersini S et al (2016) Bioprinting 3D microfibrous scaffolds for engineering endothelialized myocardium and heart-on-a-chip. Biomaterials 110:45–59. https://doi.org/10.1016/j.biomaterials.2016.09.003

Duan B (2017) State-of-the-art review of 3D bioprinting for cardiovascular tissue engineering. Ann Biomed Eng 45(1):195–209. https://doi.org/10.1007/s10439-016-1607-5

Lee W, Hong Y, Dai G (2019) 3D bioprinting of vascular conduits for pediatric congenital heart repairs. Transl Res 211:35–45. https://doi.org/10.1016/j.trsl.2019.03.007

Mao Q, Wang Y, Li Y et al (2020) Fabrication of liver microtissue with liver decellularized extracellular matrix (dECM) bioink by digital light processing (DLP) bioprinting. Mater Sci Eng C 109. https://doi.org/10.1016/j.msec.2020.110625

Lee H, Chae S, Kim JJY et al (2019) Cell-printed 3D liver-on-a-chip possessing a liver microenvironment and biliary system. Biofabrication 11(2):2. https://doi.org/10.1088/1758-5090/aaf9fa

Schneeberger K, Spee B, Costa P et al (2017) Converging biofabrication and organoid technologies: The next frontier in hepatic and intestinal tissue engineering? Biofabrication 9(1). https://doi.org/10.1088/1758-5090/aa6121

Lee SJ, Esworthy T, Stake S et al (2018) Advances in 3D bioprinting for neural tissue engineering. Adv Biosyst 2(4). https://doi.org/10.1002/adbi.201700213

Knowlton S, Cho Y, Li XJ et al (2016) Utilizing stem cells for three-dimensional neural tissue engineering. Biomater Sci 4(5):768–784. https://doi.org/10.1039/c5bm00324e

Hsieh FY, Lin HH, Hui Hsu S (2015) 3D bioprinting of neural stem cell-laden thermoresponsive biodegradable polyurethane hydrogel and potential in central nervous system repair. Biomaterials 71:48–57. https://doi.org/10.1016/j.biomaterials.2015.08.028

Yan WC, Davoodi P, Vijayavenkataraman S et al (2018) 3D bioprinting of skin tissue: from pre-processing to final product evaluation. Adv Drug Deliv Rev. https://doi.org/10.1016/j.addr.2018.07.016

Vijayavenkataraman S, Lu WF, Fuh JYH (2016) 3D bioprinting of skin: a state-of-the-art review on modelling, materials, and processes. Biofabrication 8(3). https://doi.org/10.1088/1758-5090/8/3/032001

Seol YJ, Lee H, Copus JS et al (2018) 3D bioprinted biomask for facial skin reconstruction. Bioprinting 10. https://doi.org/10.1016/j.bprint.2018.e00028

Ng WL, Wang S, Yeong WY et al (2016) Skin bioprinting: Impending reality or fantasy? Trends Biotechnol 34(9):689–699. https://doi.org/10.1016/j.tibtech.2016.04.006

Kim WJ, Lee H, Lee JU et al (2020) Efficient myotube formation in 3D bioprinted tissue construct by biochemical and topographical cues. Biomaterials 230:119632. https://doi.org/10.1016/j.biomaterials.2019.119632

Laternser S, Keller H, Leupin O et al (2018) A novel microplate 3D bioprinting platform for the engineering of muscle and tendon tissues. SLAS Technol 23(6):599–613. https://doi.org/10.1177/2472630318776594

Arrigoni C, Petta D, Bersini S et al (2019) Engineering complex muscle-tissue interfaces through microfabrication. Biofabrication 11(3):032004. https://doi.org/10.1088/1758-5090/ab1e7c

Ostrovidov S, Salehi S, Costantini M et al (2019) 3D bioprinting in skeletal muscle tissue engineering. Small 15(24). https://doi.org/10.1002/smll.201805530

Onofrillo C, Duchi S, O’Connell CD et al (2018) Biofabrication of human articular cartilage: a path towards the development of a clinical treatment. Biofabrication 10(4):4. https://doi.org/10.1088/1758-5090/aad8d9

Mouser VHM, Levato R, Bonassar LJ et al (2017) Three-dimensional bioprinting and its potential in the field of articular cartilage regeneration. Cartilage 8(4):327–340. https://doi.org/10.1177/1947603516665445

Piluso S, Li Y, Abinzano F et al (2019) Mimicking the articular joint with in vitro models. Trends Biotechnol 37(10):1063–1077. https://doi.org/10.1016/j.tibtech.2019.03.003

Groen WM, Diloksumpan P, van Weeren PR et al (2017) From intricate to integrated: biofabrication of articulating joints. J Orthop Res 35(10):2089–2097. https://doi.org/10.1002/jor.23602

Levato R, Webb WR, Otto IA et al (2017) The bio in the ink: cartilage regeneration with bioprintable hydrogels and articular cartilage-derived progenitor cells. Acta Biomater 61:41–53. https://doi.org/10.1016/j.actbio.2017.08.005

O’Connell G, Garcia J, Amir J (2017) 3D bioprinting: new directions in articular cartilage tissue engineering. ACS Biomater Sci Eng 3(11):2657–2668. https://doi.org/10.1021/acsbiomaterials.6b00587

Daly AC, Freeman FE, Gonzalez-Fernandez T et al (2017) 3D bioprinting for cartilage and osteochondral tissue engineering. Adv Healthc Mater 6(22):1700298. https://doi.org/10.1002/adhm.201700298

Antich C, de Vicente J, Jiménez G et al (2020) Bio-inspired hydrogel composed of hyaluronic acid and alginate as a potential bioink for 3D bioprinting of articular cartilage engineering constructs. Acta Biomater 106:114–123. https://doi.org/10.1016/j.actbio.2020.01.046

Bedir T, Ulag S, Ustundag CB et al (2020) 3D bioprinting applications in neural tissue engineering for spinal cord injury repair. Mater Sci Eng C 110:110741. https://doi.org/10.1016/j.msec.2020.110741

Singh NK, Han W, Nam SA et al (2020) Three-dimensional cell-printing of advanced renal tubular tissue analogue. Biomaterials 232. https://doi.org/10.1016/j.biomaterials.2019.119734

Nam H, Mi Choi Y, Jang J (2020) Vascularized lower respiratory-physiology-on-a-chip. Appl Sci 10(3). https://doi.org/10.3390/app10030900

Park JY, Ryu H, Lee B et al (2019) Development of a functional airway-on-a-chip by 3D cell printing. Biofabrication 11(1):1. https://doi.org/10.1088/1758-5090/aae545

Fishman JM, Wiles K, Lowdell MW et al (2014) Airway tissue engineering: an update. Expert Opin Biol Ther 14(10):1477–1491. https://doi.org/10.1517/14712598.2014.938631

Carter SSD, Costa PF, Vaquette C et al (2017) Additive biomanufacturing: an advanced approach for periodontal tissue regeneration. Ann Biomed Eng 45(1):12–22. https://doi.org/10.1007/s10439-016-1687-2

Ke D, Yi H, Est-Witte S et al (2020) Bioprinted trachea constructs with patient-matched design, mechanical and biological properties. Biofabrication 12(1):015022. https://doi.org/10.1088/1758-5090/ab5354

Lee JS, Kim BS, Seo D et al (2017) Three-dimensional cell printing of large-volume tissues: application to ear regeneration. Tissue Eng Part C Methods 23(3):136–145. https://doi.org/10.1089/ten.tec.2016.0362

Hakobyan D, Médina C, Dusserre N et al (2020) Laser-assisted 3D bioprinting of exocrine pancreas spheroid models for cancer initiation study. Biofabrication 12(3):035001. https://doi.org/10.1088/1758-5090/ab7cb8

Yue Z, Liu X, Coates PT et al (2016) Advances in printing biomaterials and living cells: Implications for islet cell transplantation. Curr Opin Organ Transpl 21(5):467–475. https://doi.org/10.1097/MOT.0000000000000346

Kim J, Kang K, Drogemuller CJ et al (2019) Bioprinting an artificial pancreas for type 1 diabetes. Curr Diab Rep 19(8). https://doi.org/10.1007/s11892-019-1166-x

Kim HKH-KH, Park MNM-N, Kim J et al (2019) Characterization of cornea-specific bioink: high transparency, improved in vivo safety. J Tissue Eng 10. https://doi.org/10.1177/2041731418823382

Shi P, Edgar TYS, Yeong WY et al (2017) Hybrid three-dimensional (3D) bioprinting of retina equivalent for ocular research. Int J Bioprint 3(2):138–146. https://doi.org/10.18063/IJB.2017.02.008

Tan YSE, Shi PJ, Choo CJ et al(2018) Tissue engineering of retina and Bruch’s membrane: a review of cells, materials and processes. Br J Ophthalmol 102(9):1182–1187. https://doi.org/10.1136/bjophthalmol-2017-311390

Duan B, Hockaday LA, Kang KH et al (2013) 3D Bioprinting of heterogeneous aortic valve conduits with alginate/gelatin hydrogels. J Biomed Mater Res Part A 101A(5):1255–1264. https://doi.org/10.1002/jbm.a.34420

Duchamp M, Liu T, van Genderen AM et al (2019) Sacrificial bioprinting of a mammary ductal carcinoma model. Biotechnol J 14(10):10. https://doi.org/10.1002/biot.201700703

Wang Y, Shi W, Kuss M et al (2018) 3D bioprinting of breast cancer models for drug resistance study. ACS Biomater Sci Eng 4(12):4401–4411. https://doi.org/10.1021/acsbiomaterials.8b01277

Bahcecioglu G, Basara G, Ellis BW et al (2020) Breast cancer models: engineering the tumor microenvironment. Acta Biomater 106:1–21. https://doi.org/10.1016/j.actbio.2020.02.006

Roberts S, Peyman S, Speirs V (2019) Current and emerging 3D models to study breast cancer. Adv Exp Med Biol 1152:413–427. https://doi.org/10.1007/978-3-030-20301-6_22

Cui H, Esworthy T, Zhou X et al (2020) Engineering a novel 3D printed vascularized tissue model for investigating breast cancer metastasis to bone. Adv Healthc Mater 9(15). https://doi.org/10.1002/adhm.201900924

Leonard F, Godin B (2016) 3D in vitro model for breast cancer research using magnetic levitation and bioprinting method. In: Cao J (ed) Breast Cancer. Methods in molecular biology, vol 1406. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3444-7_21

Zhou X, Zhu W, Nowicki M et al (2016) 3D bioprinting a cell-laden bone matrix for breast cancer metastasis study. ACS Appl Mater Interf 8(44):30017–30026. https://doi.org/10.1021/acsami.6b10673

Votanopoulos KI, Mazzocchi A, Sivakumar H et al (2019) Appendiceal cancer patient-specific tumor organoid model for predicting chemotherapy efficacy prior to initiation of treatment: a feasibility study. Ann Surg Oncol 26(1):139–147. https://doi.org/10.1245/s10434-018-7008-2

Mazzocchi AR, Rajan SAP, Votanopoulos KI et al(2018) In vitro patient-derived 3D mesothelioma tumor organoids facilitate patient-centric therapeutic screening. Sci Rep 8(1). https://doi.org/10.1038/s41598-018-21200-8

van Tienderen GS, Berthel M, Yue Z et al (2018) Advanced fabrication approaches to controlled delivery systems for epilepsy treatment. Expert Opin Drug Deliv 15(9):915–925. https://doi.org/10.1080/17425247.2018.1517745

Ravnic DJ, Leberfinger AN, Ozbolat IT (2017) Bioprinting and cellular therapies for type 1 diabetes. Trends Biotechnol 35(11):1025–1034. https://doi.org/10.1016/j.tibtech.2017.07.006

Votanopoulos KI, Forsythe S, Sivakumar H et al (2020) Model of patient-specific immune-enhanced organoids for immunotherapy screening: feasibility study. Ann Surg Oncol 27(6):1956–1967. https://doi.org/10.1245/s10434-019-08143-8

Albanna M, Binder KW, Murphy SV et al (2019) In situ bioprinting of autologous skin cells accelerates wound healing of extensive excisional full-thickness wounds. Sci Rep 9(1):1. https://doi.org/10.1038/s41598-018-38366-w

Van Kogelenberg S, Yue Z, Dinoro JN et al (2018) Three-dimensional printing and cell therapy for wound repair. Adv Wound Care 7(5):145–155. https://doi.org/10.1089/wound.2017.0752

Mazzocchi A, Votanopoulos K, Skardal A (2018) Personalizing cancer treatments empirically in the laboratory: patient-specific tumor organoids for optimizing precision medicine. Curr Stem Cell Rep 4(2):97–104. https://doi.org/10.1007/s40778-018-0122-z

Nie J, Gao Q, Fu J et al (2020) Grafting of 3D bioprinting to in vitro drug screening: a review. Adv Healthc Mater 9(7). https://doi.org/10.1002/adhm.201901773

Mazzocchi A, Soker S, Skardal A (2019) 3D bioprinting for high-throughput screening: drug screening, disease modeling, and precision medicine applications. Appl Phys Rev 6(1). https://doi.org/10.1063/1.5056188

Satpathy A, Datta P, Wu Y et al (2018) Developments with 3D bioprinting for novel drug discovery. Expert Opin Drug Discov 13(12):1115–1129. https://doi.org/10.1080/17460441.2018.1542427

Wu Y, Ravnic DJ, Ozbolat IT (2020) Intraoperative bioprinting: repairing tissues and organs in a surgical setting. Trends Biotechnol 38(6):594–605. https://doi.org/10.1016/j.tibtech.2020.01.004

Gu Q, Zhu H, Li J et al (2016) Three-dimensional bioprinting speeds up smart regenerative medicine. Natl Sci Rev 3(3):331–344. https://doi.org/10.1093/nsr/nww037

Zhao H, Yang F, Fu J et al (2017) Printing@Clinic: from medical models to organ implants. ACS Biomater Sci Eng 3(12):3083–3097. https://doi.org/10.1021/acsbiomaterials.7b00542

Shafiee A, Atala A (2016) Printing technologies for medical applications. Trends Mol Med 22(3):254–265. https://doi.org/10.1016/j.molmed.2016.01.003

Martelli N, Serrano C, Van Den Brink H et al (2016) Advantages and disadvantages of 3-dimensional printing in surgery: a systematic review. Surgery 159(6):1485–1500. https://doi.org/10.1016/j.surg.2015.12.017

Yi HG, Lee H, Cho DW (2017) 3D printing of organs-on-chips. Bioengineering 4(1). https://doi.org/10.3390/bioengineering4010010

Gungor-Ozkerim PS, Inci I, Zhang YS et al (2018) Bioinks for 3D bioprinting: an overview. Biomater Sci 6(5):915–946. https://doi.org/10.1039/c7bm00765e

Cui X, Li J, Hartanto Y et al (2020) Advances in extrusion 3D bioprinting: a focus on multicomponent hydrogel-based bioinks. Adv Healthc Mater 9(15). https://doi.org/10.1002/adhm.201901648

Moroni L, Burdick JA, Highley C et al (2018) Biofabrication strategies for 3D in vitro models and regenerative medicine. Nat Rev Mater 3(5):21–37. https://doi.org/10.1038/s41578-018-0006-y

Prendergast ME, Solorzano RD, Cabrera D (2017) Bioinks for biofabrication: current state and future perspectives. J 3D Print Med 1(1):49–62. https://doi.org/10.2217/3dp-2016-0002

Skeldon G, Lucendo-Villarin B, Shu W (2018) Three-dimensional bioprinting of stem-cell derived tissues for human regenerative medicine. Philos Trans R Soc B Biol Sci 373(1750). https://doi.org/10.1098/rstb.2017.0224

Leberfinger AN, Ravnic DJ, Dhawan A et al (2017) Concise review: bioprinting of stem cells for transplantable tissue fabrication. Stem Cells Transl Med 6(10):1940–1948. https://doi.org/10.1002/sctm.17-0148

Zhang YS, Pi Q, van Genderen AM (2017) Microfluidic bioprinting for engineering vascularized tissues and organoids. J Vis Exp 126:2017. https://doi.org/10.3791/55957

Corbett DC, Olszewski E, Stevens K (2019) A FRESH take on resolution in 3D bioprinting. Trends Biotechnol 37(11):1153–1155. https://doi.org/10.1016/j.tibtech.2019.09.003

Mirdamadi E, Tashman JW, Shiwarski DJ et al (2020) FRESH 3D bioprinting a full-size model of the human heart. ACS Biomater Sci Eng 6(11):6453–6459. https://doi.org/10.1021/acsbiomaterials.0c01133

Parfenov VA, Khesuani YD, Petrov SV et al (2020) Magnetic levitational bioassembly of 3D tissue construct in space. Sci Adv 6(29). https://doi.org/10.1126/sciadv.aba4174

Ghidini T (2018) Regenerative medicine and 3D bioprinting for human space exploration and planet colonisation. J Thorac Dis 10(Suppl 20):S2363–S2375. https://doi.org/10.21037/jtd.2018.03.19

Jia W, Gungor-Ozkerim PS, Zhang YS et al (2016) Direct 3D bioprinting of perfusable vascular constructs using a blend bioink. Biomaterials 106:58–68. https://doi.org/10.1016/j.biomaterials.2016.07.038

Zhang YS, Davoudi F, Walch P et al (2016) Bioprinted thrombosis-on-a-chip. Lab Chip 16(21):4097–4105. https://doi.org/10.1039/c6lc00380j

Rezaei Nejad H, Goli Malekabadi Z, Kazemzadeh Narbat M et al (2016) Laterally confined microfluidic patterning of cells for engineering spatially defined vascularization. Small 12(37):5132–5139. https://doi.org/10.1002/smll.201601391

Massa S, Sakr MA, Seo J et al (2017) Bioprinted 3D vascularized tissue model for drug toxicity analysis. Biomicrofluidics 11(4):4. https://doi.org/10.1063/1.4994708

Skardal A, Devarasetty M, Kang H-WHW et al (2016) Bioprinting cellularized constructs using a tissue-specific hydrogel bioink. J Vis Exp 2016(110):110. https://doi.org/10.3791/53606

Xu T, Binder KW, Albanna MZ et al (2013) Hybrid printing of mechanically and biologically improved constructs for cartilage tissue engineering applications. Biofabrication 5(1). https://doi.org/10.1088/1758-5082/5/1/015001

Merceron TK, Burt M, Seol YJ et al (2015) A 3D bioprinted complex structure for engineering the muscle-tendon unit. Biofabrication 7(3). https://doi.org/10.1088/1758-5090/7/3/035003

Xu C, Zhang M, Huang Y et al (2014) Study of droplet formation process during drop-on-demand inkjetting of living cell-laden bioink. Langmuir 30(30):9130–9138. https://doi.org/10.1021/la501430x

Christensen K, Xu C, Chai W et al (2015) Freeform inkjet printing of cellular structures with bifurcations. Biotechnol Bioeng 112(5):1047–1055. https://doi.org/10.1002/bit.25501

Xiong R, Christensen K, Xu C et al (2014) Jet-based 3D printing of biological constructs. In: 25th Annual International Solid Freeform Fabrication Symposium—an Additive Manufacturing Conference, pp 1069–1075

Xu C, Zhang Z, Fu J et al (2013) Time-resolved study of droplet formation process during inkjetting of alginate solution. In: 24th International Solid Freedom Fabrication Symposium—an Additive Manufacturing Conference, pp 253–259

Zhang Z, Jin Y, Yin J et al (2018) Evaluation of bioink printability for bioprinting applications. Appl Phys Rev 5(4). https://doi.org/10.1063/1.5053979

Zhang Z, Xu C, Xiong R et al (2017) Effects of living cells on the bioink printability during laser printing. Biomicrofluidics 11(3). https://doi.org/10.1063/1.4985652

Xu C, Zhang Z, Christensen K et al (2014) Freeform vertical and horizontal fabrication of alginate-based vascular-like tubular constructs using inkjetting. J Manuf Sci Eng Trans ASME 136(6). https://doi.org/10.1115/1.4028578

Gao Q, He Y, Zhong Fu J et al (2015) Coaxial nozzle-assisted 3D bioprinting with built-in microchannels for nutrients delivery. Biomaterials 61:203–215. https://doi.org/10.1016/j.biomaterials.2015.05.031

Gao Q, Liu Z, Lin Z et al (2017) 3D bioprinting of vessel-like structures with multilevel fluidic channels. ACS Biomater Sci Eng 3(3):399–408. https://doi.org/10.1021/acsbiomaterials.6b00643

Shao H, Yang X, He Y et al (2015) Bioactive glass-reinforced bioceramic ink writing scaffolds: sintering, microstructure and mechanical behavior. Biofabrication 7(3). https://doi.org/10.1088/1758-5090/7/3/035010

Gao Q, Gu H, Zhao P et al (2018) Fabrication of electrospun nanofibrous scaffolds with 3D controllable geometric shapes. Mater Des 157:159–169. https://doi.org/10.1016/j.matdes.2018.07.042

Zhao H, Chen Y, Shao L et al (2018) Airflow-assisted 3D bioprinting of human heterogeneous microspheroidal organoids with microfluidic nozzle. Small 14(39):39. https://doi.org/10.1002/smll.201802630

Shao L, Gao Q, Zhao H et al (2018) Fiber-based mini tissue with morphology-controllable GelMA microfibers. Small 14(44). https://doi.org/10.1002/smll.201802187

Gao Q, Niu X, Shao L et al (2019) 3D printing of complex GelMA-based scaffolds with nanoclay. Biofabrication 11(13). https://doi.org/10.1088/1758-5090/ab0cf6

Xie M, Gao Q, Zhao H et al (2019) Electro-assisted bioprinting of low-concentration GelMA microdroplets. Small 15(4):4. https://doi.org/10.1002/smll.201804216

Shao H, Liu A, Ke X et al (2017) 3D robocasting magnesium-doped wollastonite/TCP bioceramic scaffolds with improved bone regeneration capacity in critical sized calvarial defects. J Mater Chem B 5(16):2941–2951. https://doi.org/10.1039/c7tb00217c

Wang X, Zhang L, Ke X et al (2015) 45S5 Bioglass analogue reinforced akermanite ceramic favorable for additive manufacturing mechanically strong scaffolds. RSC Adv 5(124):102727–102735. https://doi.org/10.1039/c5ra19272b

Gao Q, He Y, Zhong Fu J et al (2016) Fabrication of shape controllable alginate microparticles based on drop-on-demand jetting. J Sol Gel Sci Technol 77(3):610–619. https://doi.org/10.1007/s10971-015-3890-2

Shao L, Gao Q, Xie C et al (2019) Bioprinting of cell-laden microfiber: Can it become a standard product? Adv Healthc Mater 8(9). https://doi.org/10.1002/adhm.201900014

Xie M, Gao Q, Qiu J et al (2020) 3D biofabrication of microfiber-laden minispheroids: a facile 3D cell co-culturing system. Biomater Sci 8(1):109–117. https://doi.org/10.1039/c9bm01189g

Shao L, Gao Q, Xie C et al (2020) Sacrificial microgel-laden bioink-enabled 3D bioprinting of mesoscale pore networks. Bio-Design Manuf 3(1):30–39. https://doi.org/10.1007/s42242-020-00062-y

Xie M, Yu K, Sun Y et al (2019) Protocols of 3D bioprinting of gelatin methacryloyl hydrogel based bioinks. J Vis Exp 2019(154). https://doi.org/10.3791/60545

Xie C, Gao Q, Wang Pet al (2019) Structure-induced cell growth by 3D printing of heterogeneous scaffolds with ultrafine fibers. Mater Des 181. https://doi.org/10.1016/j.matdes.2019.108092

Gao Q, Zhao P, Zhou R et al (2019) Rapid assembling organ prototypes with controllable cell-laden multi-scale sheets. Bio-Des Manuf 2(1):1–9. https://doi.org/10.1007/s42242-019-00032-z

Gao Q, Xie C, Wang P et al (2020) 3D printed multi-scale scaffolds with ultrafine fibers for providing excellent biocompatibility. Mater Sci Eng C 107. https://doi.org/10.1016/j.msec.2019.110269

Jin Y, Gao Q, Xie C et al (2020) Fabrication of heterogeneous scaffolds using melt electrospinning writing: design and optimization. Mater Des 185. https://doi.org/10.1016/j.matdes.2019.108274

Shao L, Gao Q, Xie C et al (2020) Synchronous 3D bioprinting of large-scale cell-laden constructs with nutrient networks. Adv Healthc Mater 9(15). https://doi.org/10.1002/adhm.201901142

Li X, Liu B, Pei B et al (2020) Inkjet bioprinting of biomaterials. Chem Rev 120(19):10793–10833. https://doi.org/10.1021/acs.chemrev.0c00008

Zhou D, Chen J, Liu B et al (2019) Bioinks for jet-based bioprinting. Bioprinting 16(May):e00060. https://doi.org/10.1016/j.bprint.2019.e00060

Zhang X, Kim GJ, Kang MG et al (2018) Marine biomaterial-Based bioinks for generating 3D printed tissue constructs. Mar Drugs 16(12):12. https://doi.org/10.3390/md16120484

Dai X, Liu L, Ouyang J et al (2017) Coaxial 3D bioprinting of self-assembled multicellular heterogeneous tumor fibers. Sci Rep 7(1):1–12. https://doi.org/10.1038/s41598-017-01581-y

Wang X, Li X, Ding J et al (2021) 3D bioprinted glioma microenvironment for glioma vascularization. J Biomed Mater Res Part A 109(6):915–925. https://doi.org/10.1002/jbm.a.37082

Wang X, Zhang X, Dai X et al (2018) Tumor-like lung cancer model based on 3D bioprinting. 3 Biotech 8(12). https://doi.org/10.1007/s13205-018-1519-1

Zhou D, Chen L, Ding J et al (2020) A 3D engineered scaffold for hematopoietic progenitor/stem cell co-culture in vitro. Sci Rep 10(1):1–11. https://doi.org/10.1038/s41598-020-68250-5

Li X, Liu L, Zhang X et al (2018) Research and development of 3D printed vasculature constructs. Biofabrication 10(3). https://doi.org/10.1088/1758-5090/aabd56

Park JY, Jang J, Kang HW (2018) 3D Bioprinting and its application to organ-on-a-chip. Microelectron Eng 200:1–11. https://doi.org/10.1016/j.mee.2018.08.004

Lee H, Han W, Kim H et al (2017) Development of liver decellularized extracellular matrix bioink for three-dimensional cell printing-based liver tissue engineering. Biomacromol 18(4):1229–1237. https://doi.org/10.1021/acs.biomac.6b01908

Jang J, Park HJ, Kim SW et al (2017) 3D printed complex tissue construct using stem cell-laden decellularized extracellular matrix bioinks for cardiac repair. Biomaterials 112:264–274. https://doi.org/10.1016/j.biomaterials.2016.10.026

Das S, Kim SW, Choi YJ et al (2019) Decellularized extracellular matrix bioinks and the external stimuli to enhance cardiac tissue development in vitro. Acta Biomater 95:188–200. https://doi.org/10.1016/j.actbio.2019.04.026

Kundu J, Shim JH, Jang J et al (2015) An additive manufacturing-based PCL-alginate-chondrocyte bioprinted scaffold for cartilage tissue engineering. J Tissue Eng Regen Med 9(11):1286–1297. https://doi.org/10.1002/term.1682

Gao G, Lee JH, Jang J et al (2017) Tissue engineered bio-blood-vessels constructed using a tissue-specific bioink and 3D coaxial cell printing technique: a novel therapy for ischemic disease. Adv Funct Mater 27(33):33. https://doi.org/10.1002/adfm.201700798

Gao G, Park JY, Kim BS et al (2018) Coaxial cell printing of freestanding, perfusable, and functional in vitro vascular models for recapitulation of native vascular endothelium pathophysiology. Adv Healthc Mater 7(23). https://doi.org/10.1002/adhm.201801102

Park J, Lee K-PKP, Kim HKHK et al (2019) Biocompatibility evaluation of bioprinted decellularized collagen sheet implanted in vivo cornea using swept-source optical coherence tomography. J Biophotonics 12(11):11. https://doi.org/10.1002/jbio.201900098

Groll J, Boland T, Blunk T et al (2016) Biofabrication: reappraising the definition of an evolving field. Biofabrication 8(1). https://doi.org/10.1088/1758-5090/8/1/013001

Groll J, Burdick JA, Cho DW et al (2019) A definition of bioinks and their distinction from biomaterial inks. Biofabrication 11(1):013001. https://doi.org/10.1088/1758-5090/aaec52

Malda J, Visser J, Melchels FP et al (2013) 25th anniversary article: Engineering hydrogels for biofabrication. Adv Mater 25(36):5011–5028. https://doi.org/10.1002/adma.201302042

Nichol JW, Koshy ST, Bae H et al (2010) Cell-laden microengineered gelatin methacrylate hydrogels. Biomaterials 31(21):5536–5544. https://doi.org/10.1016/j.biomaterials.2010.03.064

Yue K, Trujillo-de Santiago G, Alvarez MM et al (2015) Synthesis, properties, and biomedical applications of gelatin methacryloyl (GelMA) hydrogels. Biomaterials 73:254–271. https://doi.org/10.1016/j.biomaterials.2015.08.045

Pati F, Jang J, Ha DH et al (2014) Printing three-dimensional tissue analogues with decellularized extracellular matrix bioink. Nat Commun 5. https://doi.org/10.1038/ncomms4935

Ozbolat IT, Yu Y (2013) Bioprinting toward organ fabrication: challenges and future trends. IEEE Trans Biomed Eng 60(3):691–699. https://doi.org/10.1109/TBME.2013.2243912

Ozbolat IT (2015) Bioprinting scale-up tissue and organ constructs for transplantation. Trends Biotechnol 33(7):395–400. https://doi.org/10.1016/j.tibtech.2015.04.005

Yeong WY, Chua CK, Leong KF et al (2007) Comparison of drying methods in the fabrication of collagen scaffold via indirect rapid prototyping. J Biomed Mater Res Part B Appl Biomater 82(1):260–266. https://doi.org/10.1002/jbm.b.30729

Lee JM, Yeong WY (2016) Design and printing strategies in 3D bioprinting of cell-hydrogels: a review. Adv Healthc Mater 5(22):2856–2865. https://doi.org/10.1002/adhm.201600435

Xu C, Chai W, Huang Y et al (2012) Scaffold-free inkjet printing of three-dimensional zigzag cellular tubes. Biotechnol Bioeng 109(12):3152–3160. https://doi.org/10.1002/bit.24591

Riggs BC, Dias AD, Schiele NR et al (2011) Matrix-assisted pulsed laser methods for biofabrication. MRS Bull 36(12):1043–1050. https://doi.org/10.1557/mrs.2011.276

Tan KH, Chua CK, Leong KF et al (2003) Scaffold development using selective laser sintering of polyetheretherketone-hydroxyapatite biocomposite blends. Biomaterials 24(18):3115–3123. https://doi.org/10.1016/S0142-9612(03)00131-5

Suntornnond R, An J, Chua CK (2017) Bioprinting of thermoresponsive hydrogels for next generation tissue engineering: a review. Macromol Mater Eng 302(1). https://doi.org/10.1002/mame.201600266

Izadifar Z, Chang T, Kulyk W et al (2016) Analyzing biological performance of 3D-printed, cell-impregnated hybrid constructs for cartilage tissue engineering. Tissue Eng Part C Methods 22(3):173–188. https://doi.org/10.1089/ten.tec.2015.0307

Li MG, Tian XY, Chen XB (2009) A brief review of dispensing-based rapid prototyping techniques in tissue scaffold fabrication: role of modeling on scaffold properties prediction. Biofabrication 1(3). https://doi.org/10.1088/1758-5082/1/3/032001

He Y, Wu Y, Fu JZ et al (2016) Developments of 3D printing microfluidics and applications in chemistry and biology: a review. Electroanalysis 28(8):1658–1678. https://doi.org/10.1002/elan.201600043

Woodfield TBF, Malda J, De Wijn J et al (2004) Design of porous scaffolds for cartilage tissue engineering using a three-dimensional fiber-deposition technique. Biomaterials 25(18):4149–4161. https://doi.org/10.1016/j.biomaterials.2003.10.056

Kang HW, Lee SJ, Ko IK et al (2016) A 3D bioprinting system to produce human-scale tissue constructs with structural integrity. Nat Biotechnol 34(3):312–319. https://doi.org/10.1038/nbt.3413

Hochleitner G, Jüngst T, Brown TD et al (2015) Additive manufacturing of scaffolds with sub-micron filaments via melt electrospinning writing. Biofabrication 7(3). https://doi.org/10.1088/1758-5090/7/3/035002

Jang J, Park JY, Gao G et al (2018) Biomaterials-based 3D cell printing for next-generation therapeutics and diagnostics. Biomaterials 156:88–106. https://doi.org/10.1016/j.biomaterials.2017.11.030

Kim GH, Son JG, Park S et al (2008) Hybrid process for fabricating 3D hierarchical scaffolds combining rapid prototyping and electrospinning. Macromol Rapid Commun 29(19):1577–1581. https://doi.org/10.1002/marc.200800277

Hong N, Yang GH, Lee J et al (2018) 3D bioprinting and its in vivo applications. J Biomed Mater Res Part B Appl Biomater 106(1):444–459. https://doi.org/10.1002/jbm.b.33826

Mironov V, Visconti RP, Kasyanov V et al (2009) Organ printing: tissue spheroids as building blocks. Biomaterials 30(12):2164–2174. https://doi.org/10.1016/j.biomaterials.2008.12.084

Skardal A, Mack D, Kapetanovic E et al (2012) Bioprinted amniotic fluid-derived stem cells accelerate healing of large skin wounds. Stem Cells Transl Med 1(11):792–802. https://doi.org/10.5966/sctm.2012-0088

Devarasetty M, Mazzocchi AR, Skardal A (2018) Applications of bioengineered 3D tissue and tumor organoids in drug development and precision medicine: current and future. BioDrugs 32(1):53–68. https://doi.org/10.1007/s40259-017-0258-x

Cui H, Zhu W, Holmes B et al (2016) Biologically inspired smart release system based on 3D bioprinted perfused scaffold for vascularized tissue regeneration. Adv Sci 3(8). https://doi.org/10.1002/advs.201600058

Cui H, Nowicki M, Fisher JP et al (2017) 3D bioprinting for organ regeneration. Adv Healthc Mater 6(1). https://doi.org/10.1002/adhm.201601118

Chung JHY, Naficy S, Yue Z et al (2013) Bio-ink properties and printability for extrusion printing living cells. Biomater Sci. https://doi.org/10.1039/c3bm00012e

Ferris CJ, Gilmore KG, Wallace GG et al (2013) Biofabrication: an overview of the approaches used for printing of living cells. Appl Microbiol Biotechnol 97(10):4243–4258. https://doi.org/10.1007/s00253-013-4853-6

Colosi C, Shin SR, Manoharan V et al (2016) Microfluidic bioprinting of heterogeneous 3D tissue constructs using low-viscosity bioink. Adv Mater 28(4):677–684a. https://doi.org/10.1002/adma.201503310

Bertassoni LE, Cecconi M, Manoharan V et al (2014) Hydrogel bioprinted microchannel networks for vascularization of tissue engineering constructs. Lab Chip 14(13):2202–2211. https://doi.org/10.1039/c4lc00030g

Skardal A, Devarasetty M, Kang HW et al (2015) A hydrogel bioink toolkit for mimicking native tissue biochemical and mechanical properties in bioprinted tissue constructs. Acta Biomater 25:24–34. https://doi.org/10.1016/j.actbio.2015.07.030

Seol YJ, Kang HW, Lee SJ et al (2014) Bioprinting technology and its applications. Eur J Cardiothorac Surg 46(3):342–348. https://doi.org/10.1093/ejcts/ezu148

Zhu W, Webster TJ, Zhang LG (2019) 4D printing smart biosystems for nanomedicine. Nanomedicine 14(13):1643–1645. https://doi.org/10.2217/nnm-2019-0134

Shafiee A, Ghadiri E, Ramesh H et al (2019) Physics of bioprinting. Appl Phys Rev 6(2). https://doi.org/10.1063/1.5087206

Kolesky DB, Truby RL, Gladman AS et al (2014) 3D bioprinting of vascularized, heterogeneous cell-laden tissue constructs. Adv Mater 26(19):3124–3130. https://doi.org/10.1002/adma.201305506

Highley CB, Rodell CB, Burdick JA (2015) Direct 3D printing of shear-thinning hydrogels into self-healing hydrogels. Adv Mater 27(34):5075–5079. https://doi.org/10.1002/adma.201501234

Gauvin R, Chen YC, Lee JW et al (2012) Microfabrication of complex porous tissue engineering scaffolds using 3D projection stereolithography. Biomaterials 33(15):3824–3834. https://doi.org/10.1016/j.biomaterials.2012.01.048

Zorlutuna P, Annabi N, Camci-Unal G et al (2012) Microfabricated biomaterials for engineering 3D tissues. Adv Mater 24(14):1782–1804. https://doi.org/10.1002/adma.201104631

Ouyang L, Highley CB, Sun W et al (2017) A generalizable strategy for the 3D bioprinting of hydrogels from nonviscous photo-crosslinkable inks. Adv Mater 29(8). https://doi.org/10.1002/adma.201604983

Bhise NS, Manoharan V, Massa S et al (2016) A liver-on-a-chip platform with bioprinted hepatic spheroids. Biofabrication 8(1):1. https://doi.org/10.1088/1758-5090/8/1/014101

Zhang YS, Khademhosseini A (2015) Seeking the right context for evaluating nanomedicine: from tissue models in petri dishes to microfluidic organs-on-a-chip. Nanomedicine. https://doi.org/10.2217/nnm.15.18

Hou X, Zhang YS, De Santiago GT et al (2017) Interplay between materials and microfluidics. Nat Rev Mater. https://doi.org/10.1038/natrevmats.2017.16

Miri AK et al (2018) Microfluidics-enabled multimaterial maskless stereolithographic bioprinting. Adv Mater 30(27):27. https://doi.org/10.1002/adma.201800242

Pi Q, Maharjan S, Yan X et al (2018) Digitally tunable microfluidic bioprinting of multilayered cannular tissues. Adv Mater 30(43):43. https://doi.org/10.1002/adma.201706913

Ali M, AnilKumar PR, Yoo JJ et al (2019) A photo-crosslinkable kidney ECM-derived bioink accelerates renal tissue formation. Adv Healthc Mater 8(7). https://doi.org/10.1002/adhm.201800992

Skardal A, Murphy SV, Crowell K et al (2017) A tunable hydrogel system for long-term release of cell-secreted cytokines and bioprinted in situ wound cell delivery. J Biomed Mater Res Part B Appl Biomater 105(7):1986–2000. https://doi.org/10.1002/jbm.b.33736

Skardal A, Devarasetty M, Forsythe S et al (2016) A reductionist metastasis-on-a-chip platform for in vitro tumor progression modeling and drug screening. Biotechnol Bioeng 113(9):2020–2032. https://doi.org/10.1002/bit.25950

Murphy SV, Skardal A, Atala A (2013) Evaluation of hydrogels for bio-printing applications. J Biomed Mater Res Part A 101A(1):272–284. https://doi.org/10.1002/jbm.a.34326

Skardal A, Atala A (2015) Biomaterials for integration with 3-D bioprinting. Ann Biomed Eng 43(3):730–746. https://doi.org/10.1007/s10439-014-1207-1

Xu T, Olson J, Zhao W et al (2008) Characterization of cell constructs generated with inkjet printing technology using in vivo magnetic resonance imaging. J Manuf Sci Eng Trans ASME 130(2):210131–210137. https://doi.org/10.1115/1.2902857

Li Y et al (2019) High-fidelity and high-efficiency additive manufacturing using tunable pre-curing digital light processing. Addit Manuf 30. https://doi.org/10.1016/j.addma.2019.100889

Kim BS, Kim H, Gao G et al (2017) Decellularized extracellular matrix: a step towards the next generation source for bioink manufacturing. Biofabrication 9(3). https://doi.org/10.1088/1758-5090/aa7e98

Jang J, Kim TG, Kim BS et al (2016) Tailoring mechanical properties of decellularized extracellular matrix bioink by vitamin B2-induced photo-crosslinking. Acta Biomater 33:88–95. https://doi.org/10.1016/j.actbio.2016.01.013

Yi HG, Jeong YH, Kim Y et al (2019) A bioprinted human-glioblastoma-on-a-chip for the identification of patient-specific responses to chemoradiotherapy. Nat Biomed Eng 3(7):509–519

Castilho M, Feyen D, Flandes-Iparraguirre et al (2017) Melt electrospinning writing of poly-hydroxymethylglycolide-co-ε-caprolactone-based scaffolds for cardiac tissue engineering. Adv Healthc Mater 6(18):18. https://doi.org/10.1002/adhm.201700311

Stichler S, Böck T, Paxton N et al (2017) Double printing of hyaluronic acid/poly(glycidol) hybrid hydrogels with poly(ϵ-caprolactone) for MSC chondrogenesis. Biofabrication 9(4). https://doi.org/10.1088/1758-5090/aa8cb7

Castilho M, Hochleitner G, Wilson W et al (2018) Mechanical behavior of a soft hydrogel reinforced with three-dimensional printed microfibre scaffolds. Sci Rep 8(1):1. https://doi.org/10.1038/s41598-018-19502-y

de Ruijter M, Hrynevich A, Haigh JN et al (2018) Out-of-plane 3D-printed microfibers improve the shear properties of hydrogel composites. Small 14(8). https://doi.org/10.1002/smll.201702773

3D bioprinting market by component (3D bioprinters (microextrusion, inkjet, laser), bioink (natural, synthetic, hybrid)), material (hydrogel, living cells), application (skin, drug research), end user (biopharma, academia)—global forecast to 2024

3D bioprinting market—growth, trends and forecasts (2020–2025)

Aether 2020. https://discoveraether.com/

Liu W, Zhang YS, Heinrich MA et al (2017) Rapid continuous multimaterial extrusion bioprinting. Adv Mater 29(3):3. https://doi.org/10.1002/adma.201604630

Zhu K, Shin SR, van Kempen T et al (2017) Gold nanocomposite bioink for printing 3D cardiac constructs. Adv Funct Mater 27(12):12. https://doi.org/10.1002/adfm.201605352

Liu W, Zhong Z, Hu N et al (2018) Coaxial extrusion bioprinting of 3D microfibrous constructs with cell-favorable gelatin methacryloyl microenvironments. Biofabrication 10(2):2. https://doi.org/10.1088/1758-5090/aa9d44

3D Bioprinting Solutions (2020) https://bioprinting.ru/

Cyfuse Biomedical (2020) https://www.cyfusebio.com/en/

Cellbricks (2020) https://cellbricks.com/

Advanced Solutions (2020) https://www.advancedsolutions.com/

Revotek (2020) http://www.revotekhealth.com/index.aspx

Cellenion (2020) https://www.cellenion.com/

Poietis (2020) https://poietis.com/

CELLINK (2020) https://cellink.com/global/

Organovo (2020) https://organovo.com/

Grundy C, Smith R, Nickel J et al (2011) Utilization of human liver tissues for the evaluation of valproic acid-induced liver injury. 92121

BIOLIFE4D (2020) https://biolife4d.com/

Espacenet. http://www.epo.org/searching/free/espacenet.html

Colledge L (2017) Snowball metrics recipe book standardised research metrics—by the sector, for the sector. Snowball Metrics

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Acknowledgements

This study was partially supported by the collaboration agreement between the Italian Space Agency and Politecnico di Milano, “Attività di Ricerca e Innovazione” Agreement n. 2018-5-HH.0.

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Santoni, S., Gugliandolo, S.G., Sponchioni, M. et al. 3D bioprinting: current status and trends—a guide to the literature and industrial practice. Bio-des. Manuf. 5 , 14–42 (2022). https://doi.org/10.1007/s42242-021-00165-0

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Review, Analysis, and Classification of 3D Printing Literature: Types of Research and Technology Benefits

2019, AI Publication

This paper presents a review, analysis and classification about 3D printing. Through the CAPES Sucupira platform, 124 articles with a high degree of relevance published between the years 2014 and 2018 were selected. Each of these articles was classified by means of 9 categories: study types, affiliation, approach, origin of the study, geographic scope, unit of analysis, scope, benefits and negative points. Through the results obtained, it was verified that the number of articles on 3D printing is increasing every year, which indicates its importance and popularity. Most of the time, scientific research is conducted and led by people connected to universities in Europe, Asia and the Americas. And finally, the number of citations related to the benefits of 3D printing are greater than the number of citations on the negative points of the process.

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The aim of this paper is to identify, through a bibliometric analysis, how and which technologies, in the context of industry 4.0, are explicitly capable of promoting energy efficiency. The research was conducted on the Web of Science platform, using an algorithm with keywords of interest to the theme. As a result, a total of 67 articles were reached. Studies have been identified involving energy efficiency in the fields of industrial internet of things, wireless sensor networks, energy harvesting, cloud manufacturing, big data, artificial intelligence, additive manufacturing, and interdisciplinary research.

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Using lasers to 'heat and beat' 3D-printed steel could help reduce costs

Researchers have developed a new method for 3D printing metal that could help reduce costs and make more efficient use of resources.

The method, developed by a research team led by the University of Cambridge, allows structural modifications to be 'programmed' into metal alloys during 3D printing, fine-tuning their properties without the 'heating and beating' process that's been in use for thousands of years.

The new 3D printing method combines the best qualities of both worlds: the complex shapes that 3D printing makes possible, and the ability to engineer the structure and properties of metals that traditional methods allow. The results are reported in the journal Nature Communications .

3D printing has several advantages over other manufacturing methods. For example, it's far easier to produce intricate shapes using 3D printing, and it uses far less material than traditional metal manufacturing methods, making it a more efficient process. However, it also has significant drawbacks.

"There's a lot of promise around 3D printing, but it's still not in wide use in industry, mostly because of high production costs," said Dr Matteo Seita from Cambridge's Department of Engineering, who led the research. "One of the main drivers of these costs is the amount of tweaking that materials need after production."

Since the Bronze Age, metal parts have been made through a process of heating and beating. This approach, where the material is hardened with a hammer and softened by fire, allows the maker to form the metal into the desired shape and at the same time impart physical properties such as flexibility or strength.

"The reason why heating and beating is so effective is because it changes the internal structure of the material, allowing control over its properties," said Seita. "That's why it's still in use after thousands of years."

One of the major downsides of current 3D printing techniques is an inability to control the internal structure in the same way, which is why so much post-production alteration is required. "We're trying to come up with ways to restore some of that structural engineering capability without the need for heating and beating, which would in turn help reduce costs," said Seita. "If you can control the properties you want in metals, you can leverage the greener aspects of 3D printing."

Working with colleagues in Singapore, Switzerland, Finland and Australia, Seita developed a new 'recipe' for 3D printed metal that allows a high degree of control over the internal structure of the material as it is being melted by a laser.

By controlling the way that the material solidifies after melting, and the amount of heat that is generated during the process, the researchers can programme the properties of the end material. Normally, metals are designed to be strong and tough, so that they are safe to use in structural applications. 3D printed metals are inherently strong, but also brittle.

The strategy the researchers developed gives full control over both strength and toughness, by triggering a controlled reconfiguration of the microstructure when the 3D printed metal part is placed in a furnace at relatively low temperature. Their method uses conventional laser-based 3D printing technologies, but with a small tweak to the process.

"We found that the laser can be used as a 'microscopic hammer' to harden the metal during 3D printing," said Seita. "However, melting the metal a second time with the same laser relaxes the metal's structure, allowing the structural reconfiguration to take place when the part is placed in the furnace."

Their 3D printed steel, which was designed theoretically and validated experimentally, was made with alternating regions of strong and tough material, making its performance comparable to steel that's been made through heating and beating.

"We think this method could help reduce the costs of metal 3D printing, which could in turn improve the sustainability of the metal manufacturing industry," said Seita. "In the near future, we also hope to be able to bypass the low temperature treatment in the furnace, further reducing the number of steps required before using 3D printed parts in engineering applications."

The team included researchers from Nanyang Technological University, the Agency for Science, Technology and Research (A*STAR), the Paul Scherrer Institute, VTT Technical Research Centre of Finland, and the Australian Nuclear Science & Technology Organisation. Matteo Seita is a Fellow of St John's College, Cambridge.

• Materials Science
• 3-D Printing
• Engineering and Construction
• Engineering
• Inorganic Chemistry
• Civil Engineering
• Microeconomics
• Scientific method
• Three-phase electric power

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Materials provided by University of Cambridge . The original text of this story is licensed under a Creative Commons License . Note: Content may be edited for style and length.

Journal Reference :

• Shubo Gao, Zhi Li, Steven Van Petegem, Junyu Ge, Sneha Goel, Joseph Vimal Vas, Vladimir Luzin, Zhiheng Hu, Hang Li Seet, Dario Ferreira Sanchez, Helena Van Swygenhoven, Huajian Gao, Matteo Seita. Additive manufacturing of alloys with programmable microstructure and properties . Nature Communications , 2023; 14 (1) DOI: 10.1038/s41467-023-42326-y

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