Prediction of mechanical properties for hot dip galvanized steel coil based on clustering and GBDT
WANG Wei
ZHAO Fei
KUANG Zhenhui
BAI Zhenhua
LIU Yong
Abstract：The relationships among the factors affecting the mechanical properties of hot-dip galvanized steel coils are complicated,which limits the improvement of the model accuracy.In this paper,the k-means algorithm is used to cluster the galvanized steel coil data set by using the chemical composition attributes,and the data set is clustered into three pattern clusters to filter samples.The gradient boosting tree algorithm is used to research on the mechanical performance modeling of each pattern data set and the full data set without pattern division.Finally,the model parameters are optimized by combining grid search and cross-validation methods.The results show that the average MAE error of the model in the sub patterns is reduced by 0.85 MPa compared to the full data set modeling.After the parameters are optimized,the average MAE error in each mode is reduced by 5.19 MPa,and the average RMSE error is reduced by 3.63 MPa,which improves the accuracy of the prediction model.
Keywords：hot-dip galvanized steel coilsk-meansmodeling of mechanical propertiesgradient boosting de-cision treegrid search
Publication Date：2024-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 54-58 )