Prediction of martensite start temperature of steel combined with expert experience and machine learning

The martensite start temperature (M_S) plays a pivotal role in formulating heat treatment regimes for steel. This paper, through the compilation of experimental data from literature and the incorporation of expert knowledge to construct features, employs machine learning algorithms to predict the M_S of steel. The study highlights that the ETR algorithm attains optimal prediction accuracy, and the inclusion of atomic features enhances the model's performance. Feature selection is accomplished by evaluating linear and nonlinear relationships between data using the Pearson correlation coefficient (PCC), variance inflation factor (VIF), and maximum information coefficient (MIC). Subsequently, the performance of machine learning models on unknown data is compared to validate the model's generalization ability. The introduction of SHAP values for model interpretability analysis unveils the influencing mechanisms between features and the target variable. Finally, utilizing a specific steel type as an illustration, the paper underscores the practical value of the model.