Prediction model of mechanical properties of hot-rolled strip based on improved feature selection method

Selecting proper descriptors (also known feature selection, FS) is key in the process of establishing mechanical properties prediction model of hot-rolled microalloyed steels by using machine learning (ML) algorithm. FS methods based on data-driving can reduce the redundancy of data features and improve the prediction accuracy of mechanical properties. Based on the collected data of hot-rolled microalloyed steels, the association rules are used to mine the correlation information between the data. High-quality feature subsets are selected by the proposed FS method (FS method based on genetic algorithm embedding, GAMIC). Compared with the common FS method, it is shown on dataset that GAMIC selects feature subsets more appropriately. Six different ML algorithms are trained and tested for mechanical properties prediction. The result shows that the root-mean-square error of yield strength, tensile strength and elongation based on limit gradient enhancement (XGBoost) algorithm is 21.95 MPa, 20.85 MPa and 1.96%, the correlation coefficient (R²) is 0.969, 0.968 and 0.830, and the mean absolute error is 16.84 MPa, 15.83 MPa and 1.48%, respectively, showing the best prediction performance. Finally, SHapley Additive exPlanation is used to further explore the influence of feature variables on mechanical properties. GAMIC feature selection method proposed is universal, which provides a basis for the development of high-precision mechanical property prediction model.