Advances in Manufacturing ›› 2026, Vol. 14 ›› Issue (1): 103-143.doi: 10.1007/s40436-025-00545-0
• • 上一篇
Xu-Wen Zhao1, Xiao-Meng Tong1, Fang-Wei Ning1, Mao-Lin Cai1, Fei Han2, Hong-Guang Li3
收稿日期:2024-04-12
修回日期:2024-06-04
发布日期:2026-03-23
通讯作者:
Xiao-Meng Tong Email:E-mail:tongxiaomeng@buaa.edu.cn
E-mail:tongxiaomeng@buaa.edu.cn
作者简介:Xu-Wen Zhao is a doctoral candidate at School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. His main research interests are intelligent manufacturing, computer-aided engineering, and artificial intelligence.基金资助:Xu-Wen Zhao1, Xiao-Meng Tong1, Fang-Wei Ning1, Mao-Lin Cai1, Fei Han2, Hong-Guang Li3
Received:2024-04-12
Revised:2024-06-04
Published:2026-03-23
Contact:
Xiao-Meng Tong Email:E-mail:tongxiaomeng@buaa.edu.cn
E-mail:tongxiaomeng@buaa.edu.cn
Supported by:摘要： Computer-aided engineering (CAE) is widely used in the industry as an approximate numerical analysis method for solving complex engineering and product structural mechanical performance problems. However, with the increasing complexity of structural and performance requirements, the traditional research paradigm based on experimental observations, theoretical modeling, and numerical simulations faces new scientific problems and technical challenges in analysis, design, and manufacturing. Notably, the development of CAE applications in future engineering is constrained to some extent by insufficient experimental observations, lack of theoretical modeling, limited numerical analysis, and difficulties in result validation. By replacing traditional mathematical mechanics models with data-driven models, artificial intelligence (AI) methods directly use high-dimensional, high-throughput data to establish complex relationships between variables and capture laws that are difficult to discover using traditional mechanics research methods, offering significant advantages in the analysis, prediction, and optimization of complex systems. Empowering CAE with AI to find new solutions to the difficulties encountered by traditional research methods has become a developing trend in numerical simulation research. This study reviews the methods and applications of combining AI with CAE and discusses current research deficiencies as well as future research trends.
The full text can be downloaded at https://doi.org/10.1007/s40436-025-00545-0
Xu-Wen Zhao, Xiao-Meng Tong, Fang-Wei Ning, Mao-Lin Cai, Fei Han, Hong-Guang Li. Review of empowering computer-aided engineering with artificial intelligence[J]. Advances in Manufacturing, 2026, 14(1): 103-143.
Xu-Wen Zhao, Xiao-Meng Tong, Fang-Wei Ning, Mao-Lin Cai, Fei Han, Hong-Guang Li. Review of empowering computer-aided engineering with artificial intelligence[J]. Advances in Manufacturing, 2026, 14(1): 103-143.
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