Page 7 - IJAMD-2-2

International Journal of AI for
Materials and Design

REVIEW ARTICLE
Artificial intelligence-driven material

development for additive manufacturing: A
critical review

Peijie Shangguan 1 , Huifei Zhou 1 , Xi Huang 2 , Jinlong Su * ,
1
Wai Yee Yeong 2 , and Swee Leong Sing *
1
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
(This article belongs to Special Issue: Artificial Intelligence Applications in Additive Manufacturing
and 3D Printing)

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
*Corresponding authors: in artificial intelligence (AI), for example, machine learning and deep learning, have
Jinlong Su emerged as powerful tools in accelerating material discovery, optimizing process
(jinlongsu96@foxmail.com) parameters, and improving material performance for AM. This review provides
Swee Leong Sing
(sweeleong.sing@nus.edu.sg) a comprehensive overview of AI-driven material development for AM, focusing
on metals, polymers, and bioinks/biomaterial inks. The discussion encompasses
Citation: Shangguan P, Zhou H, AI techniques applied to material development, including predictive modeling,
Huang X , Su J, Yeong WY,
Sing SL. Artificial intelligence-driven generative algorithms, and intelligent optimization methods. Data collection and
material development for additive pre-processing methodologies for AI applications in AM are discussed. In addition,
manufacturing: A critical review. Int the applications of AI in material development in AM are also reviewed. Finally, the
J AI Mater Design. 2025;2(2):1-26.
doi: 10.36922/IJAMD025100007 review highlights emerging trends, such as AI-driven high-throughput material
screening, integration of AI with multiscale high-fidelity simulations, the use of digital
Received: March 5, 2025
twins for real-time process control, and active learning strategies for optimizing
1st revised: April 3, 2025 material compositions. By summarizing recent advancements and outlining future
2nd revised: April 11, 2025 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
Accepted: April 16, 2025
generation of manufacturing.
Published online: May 5, 2025
Copyright: © 2025 Author(s). Keywords: Artificial intelligence; Additive manufacturing; Machine learning; Material
This is an Open-Access article
distributed under the terms of the design; Performance optimization; Bioprinting
Creative Commons Attribution
License, permitting distribution,
and reproduction in any medium,
provided the original work is
properly cited. 1. Introduction
Publisher’s Note: AccScience Additive manufacturing (AM) has revolutionized modern manufacturing by enabling
Publishing remains neutral with the layer-by-layer fabrication of complex structures with high precision and design
regard to jurisdictional claims in
1
published maps and institutional flexibility. This approach minimizes or even eliminates the need for extensive
affiliations.

Volume 2 Issue 2 (2025) 1 doi: 10.36922/IJAMD025100007