3D-printed multifunctional materials enabled by artificial-intelligence-assisted fabrication technologies

Zhijie Zhu, Daniel Wai Hou Ng, Hyun Soo Park, Michael C. McAlpine

Research output: Contribution to journalReview articlepeer-review

106 Scopus citations


The emerging capability to 3D print a diverse palette of functional inks will enable the mass democratization of patient-specific wearable devices and smart biomedical implants for applications such as health monitoring and regenerative biomedicines. These personalized wearables could be fabricated via ex situ printing, which involves first printing a design on a planar substrate and then deploying it to the target surface. However, this can result in a geometrically and dynamically mismatched interface between printed materials and target surfaces. In situ printing provides a potential remedy by directly printing 3D constructs on the target surfaces. This new manufacturing procedure requires the assistance of artificial intelligence (AI) to sense, adapt and predict the state of the printing environment, such as a dynamically morphing organ. In this Review, we discuss electronic and biological inks for in situ 3D printing, AI-empowered 3D-printing approaches with open-loop, closed-loop and predictive control, and recent developments in surgical robotics and AI that could be integrated in future 3D-printing approaches. We anticipate that this convergence of AI, 3D printing, functional materials and personalized biomedical devices will lead to a compelling future for smart manufacturing.

Original languageEnglish (US)
Pages (from-to)27-47
Number of pages21
JournalNature Reviews Materials
Issue number1
StatePublished - Oct 12 2020

Bibliographical note

Funding Information:
M.C.M. acknowledges support by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under award number DP2EB020537. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. H.S.P. acknowledges support from the Division of Information and Intelligent Systems of the National Science Foundation (1846031). Z.Z. acknowledges support from the graduate school of the University of Minnesota (2019–20 Doctoral Dissertation Fellowship).

Publisher Copyright:
© 2020, Springer Nature Limited.


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