Abstract
In this century, deep learning has been widely used due to the rapid popularization of the Internet and the improvement of computer performance. This dataset has been established by collecting the high-cycle fatigue test data of 304 stainless steel. The datasets were preprocessed, then inputted into the back propagation neural network model, a fuzzy neural network model, and a long short-term memory neural network (LSTM) model for training and testing. The reliability and generalization of the three models have been verified by high-cycle fatigue experiments, and the prediction effects of the three models compared. The results show that the LSTM model in the deep learning model has better prediction accuracy for high-cycle fatigue life, is superior to the other two machine learning models in terms of generalization and accuracy, and the correlation coefficient (R2) of the final prediction result was 0.9786.
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Duan, H., He, H., Yue, S. et al. Analysis of High-Cycle Fatigue Life Prediction of 304 Stainless Steel Based on Deep Learning. JOM 75, 4586–4595 (2023). https://doi.org/10.1007/s11837-023-06042-8
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DOI: https://doi.org/10.1007/s11837-023-06042-8