Crown prediction of hot strip steel based on integrated feature selection and SVR
WANG Youlong1, LI Weigang1,2, WANG Yongqiang1
1. School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; 2. Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China
Abstract：As a key index to evaluate the shape quality of hot strip steel plate,the crown of hot strip steel plate has the characteristics of multi-variable,nonlinear and hereditary. The traditional crown model of hot continuous rolling plate has some problems, such as complicated mechanism, difference between theory and practice and limited accuracy of the model. In order to solve these problems, a prediction model for crown of hot continuous rolling plate based on integrated feature selection and support vector regression is proposed in this paper. Firstly, an ensemble learning model based on Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and Gradient Boosting Decision Tree (GBDT) is established, and the comprehensive application of these base learners can fully mine the feature information in the data. Secondly, the feature importance obtained by the base learner is weighted by the ensemble learning model, and the most informative input features are selected according to the feature importance ranking after fusion, which can effectively reduce the feature dimension. Then, Grey Wolf Optimization (GWO) is used to optimize the parameters in the Support Vector Regression (SVR) prediction model, which can not only eliminate the subjectivity and blindness of traditional manual parameter selection. It can also better adapt to the characteristics of the data. Finally, the selected features are input into the SVR prediction model with optimized parameters, which is used to predict the crown of hot continuous rolling plate. The experimental results show that the absolute error of the model is more than 99% within 15 μm. The prediction model not only improves the prediction accuracy, but also provides powerful guidance and support for the precise control of crown and the improvement of shape quality of hot continuous rolling plate. It provides useful methods and ideas for solving the key problems in complex hot continuous rolling production and improving the sustainability and efficiency of the production process.
王优龙, 李维刚, 王永强. 基于集成特征选择和SVR的热连轧板凸度预测[J]. 钢铁, 2024, 59(1): 99-107.
WANG Youlong, LI Weigang, WANG Yongqiang. Crown prediction of hot strip steel based on integrated feature selection and SVR[J]. Iron and Steel, 2024, 59(1): 99-107.
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2024, Vol. 59 
