A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures

• Published in: Engineering fracture mechanics Vol. 258; p. 108130
• Main Authors: Zhang, Xiao-Cheng, Gong, Jian-Guo, Xuan, Fu-Zhen
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.12.2021; Elsevier BV
• Subjects: Artificial neural networks; Constraint modelling; Creep fatigue; Deep neural network; Fatigue life; Hierarchies; High temperature; Life prediction; Machine learning; Neural networks; Physics; Physics-informed; Stainless steels; Life prediction; Deep neural network; Physics-informed; Creep-fatigue; Machine learning
• ISSN: 0013-7944; 1873-7315
• DOI: 10.1016/j.engfracmech.2021.108130

•Physics-informed neural network (PINN) is built for creep-fatigue life prediction.•Physics-informed feature engineering and physics-informed loss function are applied.•PINN exhibits better prediction capacity than conventional machine learning models. Physics-informed neural network has strong generalization ability for small dataset, due to the inclusion of underlying physical knowledge. Two strategies are enforced to incorporate physics constraints to a deep neural network in this work. One is to obtain extended features through physics-informed feature engineering, and the other is to incorporate physics-informed loss function into deep neural network as constraints. Conventional machine learning models, deep neural network and physics-informed neural network are applied to predict creep-fatigue life of 316 stainless steel. Results show that physics-informed neural network presents better prediction accuracy than deep neural network and conventional machine learning models.