文献类型：期刊文献

英文题名：Predicting the fatigue life of additively manufactured AlSi10Mg alloy using a physics-informed neural network incorporating continuous damage mechanics

作者：Wu, Qimin[1];Wang, Haijie[1];Li, Bo[1,2,3]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Minlong 130, Shanghai 200237, Peoples R China;[2]Shanghai Collaborat Innovat Ctr High end Equipment, Shanghai, Peoples R China;[3]East China Univ Sci & Technol, Addit Mfg & Intelligent Equipment Res Inst, Shanghai, Peoples R China

年份：2025

卷号：239

期号：13

起止页码：1897

外文期刊名：PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE

收录：;EI(收录号：20244417285251);WOS:【SCI-EXPANDED(收录号:WOS:001337865000001)】；

基金：The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research work is sponsored by National Natural Science Foundation of China (Grant No. 52175140), National Key R&D Program of China (Grant No. 2022YFB4602102), Fundamental Research Funds for the Central Universities in China (Grant No. JKG01231610).

语种：英文

外文关键词：Fatigue life; machine learning; continuous damage mechanics; physics-informed neural network; additive manufacturing

摘要：The material characteristics of additively manufactured AlSi10Mg alloy, including the random distribution of process-induced micro defects, microstructual anisotropy, and grain morphologies with complex diversity, poses a significant challenge in accurately predicting its fatigue life, limiting its application in the aircraft field. This work herein introduces a novel approach incorporating a physics-informed neural network (PINN), in which an artificial neural network (ANN) is embedded with a damage mechanics model (CDM) and the critical process parameters of laser powder bed fusion (L-PBF) additive manufacturing. The partial differential equations of the updated CDM model are introduced into the training procedures of the PINN to building a loss function, effectively "teaching" the PINN to learn the physical knowledge. A comparison of the fatigue life prediction results of ANN and the proposed PINN models shows that the PINN model outperforms its counterparts with a 38.71% higher prediction accuracy. The effects of L-PBF process parameters on the fatigue life of as-built AlSi10Mg is examined using both ANN and PINN, proving the better predictive performance and data-physics consistency of the PINN model. The scheme of this work can inform the efficient prediction of fatigue life of additively manufactured alloys and also reverse prediction or fast finding of optimized process parameters for additive manufacturing.