Modeling analysis of yield strength and earing ratio of sheet metal based on machine learning algorithm

In view of the high earing ratio and unstable yield strength during 1070 sheet metal production, a "composition-process-performance" model was built with a random forest algorithm based on production data. The following factors were chosen as independent variables: cold rolling reduction, hot rolling finishing temperature, Fe content, Fe/Si, etc., and the accuracies of the yield strength model (R²) and earing model were 0.75 and 0.87, respectively. Based on those models, the influence of various parameters on yield strength and earing was quantitatively studied. The variable weight coefficients and SHAP values of their variables were calculated with the model. The most remarkable factor for the yield strength was the cold rolling reduction, which was positively correlated, and the most remarkable factor for earing ratio was the Fe content, which was negatively correlated. Models were used for property predictions with given process parameters, and optimized parameters were achieved.