A deep learning-based method for predicting the low-cycle fatigue life of austenitic stainless steel

Abstract

Abstract In modern engineering, predicting the fatigue life of materials is crucial for safety assessment. The relationship between fatigue life and its influencing factors is difficult to predict by traditional methods, and deep learning can achieve great power and flexibility through nested hierarchies of concepts. Taking the low cycle fatigue life of 316 austenitic stainless steel as an example, a method for predicting the low cycle fatigue life of austenitic stainless steel by deep learning is established based on the limited ability of traditional neural network model and genetic algorithm optimization model. The deep neural network model is introduced to predict the fatigue life of the material. The results show that the prediction correlation coefficient R of the deep neural network prediction model with three hidden layers is 0.991, and the deep neural network learning model has better prediction ability.