文献类型：期刊文献

英文题名：A data-physics integrated approach to life prediction in very high cycle fatigue regime

作者：Fan, Jia-Le[1];Zhu, Gang[1];Zhu, Ming-Liang[1];Xuan, Fu-Zhen[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Sch Mech & Power Engn, Key Lab Pressure Syst & Safety, Shanghai 200237, Peoples R China

年份：2023

卷号：176

外文期刊名：INTERNATIONAL JOURNAL OF FATIGUE

收录：;EI(收录号：20233814758125);WOS:【SCI-EXPANDED(收录号:WOS:001073799300001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant No. 51835003) and by Innovation Program of Shanghai Municipal Education Commission (2023-05-49) .

语种：英文

外文关键词：Very high cycle fatigue; Fatigue life prediction; Physics-informed neural network; Micro-defect; Z -parameter model

摘要：The defects created in metallurgical and manufacturing processes generally play a decisive role in very high cycle fatigue life of engineering structures. By taking stress level, defect size and location into account, the physical Zparameter model on fatigue life prediction was combined with artificial neural network as a new data-physics integrated approach for fatigue life prediction of 15Cr and FV520B-I steels in this work. The original data from tests were expanded based on the Z-parameter model, and the physics-informed loss function featuring Zparameter was integrated into artificial neural network as the constraint. Results showed that the physicsinformed neural network established in this work could be applied for life prediction in the very high cycle fatigue regime, and the model came with higher predictive accuracy than the physical Z-parameter model and the Mayer's model did.