TY - JOUR
T1 - Recent Advances in Metal Additive Manufacturing
T2 - Materials Design and Artificial Intelligence Applications
AU - Wang, Shuo
AU - Zhou, Lin
AU - Zhong, Shiyu
AU - Li, Gan
AU - Zhang, Lei
AU - Wang, Xu
AU - Li, Zhiqiang
AU - Lu, Jian
N1 - Publisher Copyright:
© 2026 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/
PY - 2026
Y1 - 2026
N2 - Over the past 30 years, metal additive manufacturing (AM) has advanced rapidly, reaching major milestones that have transformed the manufacturing landscape. This layer-by-layer fabrication technique offers exceptional design freedom and manufacturing flexibility, delivering notable performance and economic benefits across sectors such as aerospace and the automotive industry. This study methodically analyses various printing processes, identifies common defects in metallic materials, and proposes effective strategies for their mitigation. We elucidate the complex relationships among various manufacturing methods, microstructures, and their resulting performances. Considering the rapid advancement of artificial intelligence, we outline its various applications within the field of AM. Within the framework of Industry 5.0, the integration of high-throughput experimentation and materials genome engineering (MGE) is expected to substantially expedite the discovery of novel materials. Moreover, agents developed via the integration of large language models with AM expertise are poised to provide innovative approaches for optimising process parameters and enhancing decision-making accuracy. With continued advancements in AM agents, cloud computing, renewable energy, and structural design principles, the realisation of smart AM factories based on these technologies is becoming increasingly achievable. This improvement is expected to propel metal AM into a new era characterised by intelligence and customisation, fostering substantial progress and transformative shifts in materials science and manufacturing engineering.
AB - Over the past 30 years, metal additive manufacturing (AM) has advanced rapidly, reaching major milestones that have transformed the manufacturing landscape. This layer-by-layer fabrication technique offers exceptional design freedom and manufacturing flexibility, delivering notable performance and economic benefits across sectors such as aerospace and the automotive industry. This study methodically analyses various printing processes, identifies common defects in metallic materials, and proposes effective strategies for their mitigation. We elucidate the complex relationships among various manufacturing methods, microstructures, and their resulting performances. Considering the rapid advancement of artificial intelligence, we outline its various applications within the field of AM. Within the framework of Industry 5.0, the integration of high-throughput experimentation and materials genome engineering (MGE) is expected to substantially expedite the discovery of novel materials. Moreover, agents developed via the integration of large language models with AM expertise are poised to provide innovative approaches for optimising process parameters and enhancing decision-making accuracy. With continued advancements in AM agents, cloud computing, renewable energy, and structural design principles, the realisation of smart AM factories based on these technologies is becoming increasingly achievable. This improvement is expected to propel metal AM into a new era characterised by intelligence and customisation, fostering substantial progress and transformative shifts in materials science and manufacturing engineering.
KW - Artificial intelligence
KW - Composition-process-microstructure-performance
KW - Industry 5.0
KW - Metal additive manufacturing
KW - Smart factory
KW - Structuraldesign
UR - https://www.scopus.com/pages/publications/105034439949
U2 - 10.1016/j.eng.2025.11.033
DO - 10.1016/j.eng.2025.11.033
M3 - Article
AN - SCOPUS:105034439949
SN - 2095-8099
JO - Engineering
JF - Engineering
ER -