文献类型：期刊文献

英文题名：Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods

作者：Wang, Haijie[1];Li, Bo[1];Gong, Jianguo[1];Xuan, Fu-Zhen[1,2,3]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Pressure Syst & Safety, Minist Educ, Shanghai 200237, Peoples R China;[3]Shanghai Collaborat Innovat Ctr High end Equipment, Shanghai 200237, Peoples R China

年份：2023

卷号：284

外文期刊名：ENGINEERING FRACTURE MECHANICS

收录：;EI(收录号：20231413854259);WOS:【SCI-EXPANDED(收录号:WOS:000979720800001)】；

基金：Acknowledgments This work is financially sponsored by National Natural Science Foundation of China (Grant No. 52175140) , Natural Science Foundation of Shanghai, China (Grant No. 20ZR1414000) .

语种：英文

外文关键词：Fatigue life; Machine learning; Physical theory; Data-driven; Hybrid models

摘要：Fatigue life prediction is critical for ensuring the safe service and the structural integrity of mechanical structures. Although data-driven approaches have been proven effective in predicting fatigue life, the lack of physical interpretation hinders their widespread applications. To satisfy the requirements of physical consistency, hybrid physics-informed and data-driven models (HPDM) have become an emerging research paradigm, combining physical theory and datadriven models to realize the complementary advantages and synergistic integration of physicsbased and data-driven approaches. This paper provides a comprehensive overview of datadriven approaches and their modeling process, and elaborates the HPDM according to the combination of physical and data-driven models, then systematically reviews its application in fatigue life prediction. Additionally, the future challenges and development directions of fatigue life prediction are discussed.