Low-Cycle and Thermomechanical Fatigue Life Prediction Method for Compacted Graphite Iron Based on Small-Sample Physics-Informed Neural Networks

Abstract

A physics-informed neural network (PINN) model based on deep learning has been proposed for predicting low-cycle fatigue (LCF) and thermomechanical fatigue (TMF) life. By analyzing the LCF and TMF data of compacted graphite iron (CGI), characteristic parameters were identified that can simultaneously represent both types of fatigue, achieving a unification of the parameters for the two fatigue life models. The incorporation of fatigue life physical information as a constraint in the loss function of the deep neural network enabled accurate predictions of LCF and TMF for CGI under small-sample conditions. Comparative analysis results indicated that the deep learning–based PINN model outperformed traditional machine learning models in terms of prediction accuracy. Additionally, comparisons with traditional LCF and TMF life prediction models showed that the deep learning–based PINN model achieves high prediction accuracy while possessing generalization and extrapolation capabilities unattainable by traditional models. These results demonstrate that the PINN model exhibits high accuracy and versatility.