Strip Thickness and Profile-Flatness Prediction in Tandem Hot Rolling Process Using Mechanism Model-Guided Machine Learning

This study is focused on the multivariable, nonlinear, strong-coupling, and time-varying characteristics of the quality control process of hot strip rolling. Using the rolling process data from a continuously variable crown (CVC) mill of a 1580 mm hot tandem rolling line, a new prediction model of the strip thickness, profile, and flatness in hot rolling based on rolling mechanism model-guided machine learning (ML) is developed. The weighted processing (WP) method is used to fuse the rolling mechanism and process data, to improve the relationship between the strong correlation features and the model. Combined with nondominated sorting genetic algorithm III (NSGA-III), multiple parameters of multioutput support vector regression (M-SVR) are optimized. The results show that the root mean square error (RMSE), mean square correlation coefficient (R (2)), and mean absolute error (MAE) of the strip thickness, crown, and flatness are 2.0159, 1.3191, and 0.9355; 0.9854, 0.9895, and 0.9873; and 16.601, 1.126, and 0.604, respectively. Moreover, the established method of data fusion rolling mechanism shows strong capability to improve the model prediction accuracy, increasing it by 60%. Thus, it can offer theoretical guidance for realizing accurate control of the quality of strips and improving the quality of hot-rolled products.

Automated summary

This study focuses on the complex characteristics of quality control in the hot strip rolling process. A new prediction model for strip thickness, profile, and flatness is developed using machine learning guided by a rolling mechanism model. The fusion of rolling mechanism and process data, combined with optimization algorithms, improves the accuracy of the model. The results show a significant improvement in prediction accuracy, providing theoretical guidance for controlling strip quality and improving the quality of hot-rolled products.