Abstract
An ANN model with knowledge-based features is proposed to predict multiaxial low-cycle fatigue life under irregular loading and verified on 304L stainless steel. Feature selection for knowledge-based features solves overfitting problem, improves model performance, and selects the genetic knowledge-based features. With help of genetic knowledge-based features, the proposed model combines physics knowledge and machine learning, being able to predict fatigue life of irregular cases through training only with regular cases. Most predicted results are located within 2-factor band. Besides, SHAP method interprets the ANN model, shows the contribution of features, and illustrates effectiveness of the model through the curves that fit facts.
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