A Bidirectional LSTM/GRU Based Deep Learning Method for Prediction Fatigue Life of Stainless Steels under Combined Axial and Inner Pressure Multiaxial Loading

To develop a high-accuracy fatigue life prediction model for AISI 316 and AISI 430 under combined axial and internal pressure multiaxial proportional and non-proportional loading conditions, the Bi-LSTM and Bi-GRU deep learning algorithms were employed. In addition, two conventional machine learning algorithms, namely random forest (RF) and support vector regression (SVR), were utilized for comparison. The obtained results indicate that the von Mises equivalent stress approach, as well as the RF and SVR models, failed to accurately predict fatigue life under such complex loading conditions. In contrast, the Bi-LSTM model achieved higher prediction accuracy for AISI 316, with almost all predicted data points aligning closely along the diagonal line, except for one outlier. On the other hand, the Bi-GRU model exhibited better predictive performance for AISI 430. Furthermore, 50 independent holdout splits were conducted for both Bi-LSTM and Bi-GRU models to analyze the RMSE distributions of the testing subsets. The results were found to be consistent with the predictive outcomes, confirming the reliability and stability of the developed deep learning models.