XUAN Ruifei, XU Tingfeng, ZHANG Dazheng, FENG Zhiqiang, WANG Xingyu, LI Weijuan
Traditional steel products development relies heavily on trial-and-error methods, which suffer from long cycles, high costs, and difficulties in accurately correlating chemical composition with performance. To improve efficiency and reduce costs, this study constructs an integrated model of XGBoost and LightGBMbase on production data of a steel mill to perform multi-target regression predictions for the yield strength, tensile strength, and total elongation at maximum force of HRB400E hot-rolled ribbed bars. The results indicate that the LightGBM model outperforms the others, achieving coefficients of determination (R2) of 0.953 66, 0.980 15, and 0.946 74 for yield strength, tensile strength, and total elongation at maximum force, respectively, on the test set. To further enhance model interpretability, derivative features generated by a physical metallurgical model were incorporated into the LightGBM model. On the test set, the coefficients of determination (R2) for the three properties reached 0.965 03, 0.985 52, and 0.956 30, respectively. The MAE decreased by 12.54%, 13.25%, and 6.39%, while the RMSE was reduced by 13.13%, 14.58%, and 9.39%, respectively. This data-mechanism integrated driving combines high accuracy and interpretability, providing an efficient and reliable solution for intelligent steel manufacturing.
