文献类型：期刊文献

英文题名：A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions

作者：Zhang, Xiao-Cheng[1];Gong, Jian-Guo[1];Xuan, Fu-Zhen[1]

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

年份：2021

卷号：148

外文期刊名：INTERNATIONAL JOURNAL OF FATIGUE

收录：;EI(收录号：20211310142255);WOS:【SCI-EXPANDED(收录号:WOS:000647800900004)】；

基金：Supports from National Science Foundation of China (51835503, 51605165) are greatly acknowledged.

语种：英文

外文关键词：Machine learning; Deep learning; Neural network; Life prediction; Creep-fatigue; Creep; Fatigue

摘要：Deep learning is a particular kind of machine learning, which achieves great power and flexibility by a nested hierarchy of concepts. A general life prediction method for components under creep, fatigue and creep-fatigue conditions is proposed. Fatigue, creep and creep-fatigue data of a typical austenitic stainless steel (i.e., 316) are integrated. Conventional machine learning models (e.g., support vector machine, random forest, Gaussian process regression, shallow neural network) and deep learning model (e.g., deep neural network) are applied for life predictions. Results show that deep learning model exhibits better prediction accuracy and generalization ability than conventional machine learning model.