文献类型：期刊文献

英文题名：Fatigue-life prediction of additively manufactured metals by continuous damage mechanics (CDM)-informed machine learning with sensitive features

作者：Wang, Haijie[1];Li, Bo[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：2022

卷号：164

外文期刊名：INTERNATIONAL JOURNAL OF FATIGUE

收录：;EI(收录号：20222912376547);WOS:【SCI-EXPANDED(收录号:WOS:000835277800004)】；

基金：Acknowledgement This research work is sponsored by National Natural Science Foun-dation of China (No.52175140) , International Collaboration Program from Science and Technology Commission of Shanghai Municipality in China (No.19110712500) , Natural Science Foundation of Shanghai inChina (No.20ZR1414000) .

语种：英文

外文关键词：Machine learning (ML); Fatigue life; Additive manufacturing; Sensitive features; Continuous damage mechanics

摘要：Additive manufacturing (AM) process-induced defects make the fatigue life prediction of AM-built parts chal-lenging. A machine learning (ML) framework based on sensitive features and continuous damage mechanics (CDM) herein is proposed to predict the fatigue life of AM-built parts. The sensitive features are extracted to blunt the disturbing effect of causality among the features. The CDM theory considering AM parameters is conducive to constructing a physics-informed ML model. This work employs support vector machines and random forests to predict the fatigue life of AM-built AlSi10Mg alloy. The results demonstrate that the physical knowledge-guided ML model using sensitive features exhibits better performance of fatigue life prediction.