Data Driven Strip Crown Prediction for a Hot Strip Rolling Mill Process

Abstract

Due to the difficulty in strip crown prediction caused by multivariable, nonlinear and strong coupling in the hot strip rolling mill (HSRM) process, this paper proposes a strip crown prediction model based on support vector regression (SVR), and uses sparrow search algorithm (SSA) to optimize the parameter C and $\sigma$ of the model, so as to improve the generalization ability of the prediction model. The overall performance of the model is evaluated by mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and correlation coefficient $(R^{2})$. It shows that the prediction accuracy and generalization ability of the proposed model are better than the traditional methods. The proposed SSA-SVR model in this paper is successfully applied to the crown prediction of the 2150 production line of Ansteel company. The performance shows that the method can be efficient to predict the steel crown in a real HSRM process.