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REVIEW ARTICLE

Artificial intelligence-driven material development for additive manufacturing: A critical review

Peijie Shangguan1 ,  Huifei Zhou1 ,  Xi Huang2 ,  Jinlong Su1* ,  Wai Yee Yeong2 ,  Swee Leong Sing1*
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1 Department of Mechanical Engineering, College of Design and Engineering, National University of Singapore, Singapore
2 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
IJAMD, 025100007 https://doi.org/10.36922/IJAMD025100007
Received: 5 March 2025 | Revised: 11 April 2025 | Accepted: 16 April 2025 | Published online: 5 May 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Additive manufacturing (AM) has revolutionized material fabrication by enabling the production of complex structures with enhanced design flexibility and material efficiency. However, the development of AM-specific materials remains a critical challenge due to the unique process characteristics of AM. Recent advancements in artificial intelligence (AI), for example, machine learning and deep learning, have emerged as powerful tools in accelerating material discovery, optimizing process parameters, and improving material performance for AM. This review provides a comprehensive overview of AI-driven material development for AM, focusing on metals, polymers, and bioinks/biomaterial inks. The discussion encompasses AI techniques applied to material development, including predictive modeling, generative algorithms, and intelligent optimization methods. Data collection and pre-processing methodologies for AI applications in AM are discussed. In addition, the applications of AI in material development in AM are also reviewed. Finally, the review highlights emerging trends, such as AI-driven high-throughput material screening, integration of AI with multiscale high-fidelity simulations, the use of digital twins for real-time process control, and active learning strategies for optimizing material compositions. By summarizing recent advancements and outlining future directions, this review provides insights into the evolving intersection of AI and AM, paving the way for more intelligent and efficient material development in the next generation of manufacturing.

Keywords
Artificial intelligence
Additive manufacturing
Machine learning
Material design
Performance optimization
Bioprinting
Funding
This work is fully supported by the Advanced Research and Technology Innovation Centre (ARTIC), National University of Singapore, under the grant (project number: ADT-RP1).
Conflict of interest
Swee Leong Sing serves as an Editorial Board Member of the journal and as the Guest Editor for the Special Issue to which this paper belongs, but he was not involved in the editorial or peer-review process for this manuscript, either directly or indirectly. Jinlong Su is a Youth Editorial Board Member of the journal and serves as a Co-Guest Editor for the same Special Issue, but similarly had no involvement in the editorial handling or peer-review of this paper. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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