Roll gap prediction in acceleration and deceleration process of cold rolling based on a data-driven method
Yun-jian Hu1, Jie Sun2, Huai-tao Shi1, Qing-long Wang3, Jian-zhao Cao1
1 School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, Liaoning, China 2 The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, Liaoning, China 3 Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, Hebei, China
Roll gap prediction in acceleration and deceleration process of cold rolling based on a data-driven method
Yun-jian Hu1, Jie Sun2, Huai-tao Shi1, Qing-long Wang3, Jian-zhao Cao1
1 School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, Liaoning, China 2 The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, Liaoning, China 3 Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, Hebei, China
摘要 Severe fluctuation of the effective roll gap in the acceleration and deceleration section of the cold rolling process is a significant factor causing thickness deviation. However, the conventional roll gap compensation method and control strategy do not meet the stringent strip quality requirements. The roll gap model in the acceleration and deceleration process is studied to increase the thickness control precision. In order to improve model accuracy, a roll gap prediction method based on data-driven is proposed. Given the complexities of the cold rolling process, the extreme gradient boosting (XGBoost) method is used to predict the roll gap model as the rolling speed changes. Meanwhile, support vector regression and neural network-based methods are taken to evaluate and compare the prediction performances. Based on the field data, the simulation experiments are carried out. It demonstrated that the prediction performance of the proposed method outperformed the other two methods. The values of root mean square error, determination coefficient value, mean absolute percentage error and mean absolute error obtained from the XGBoost model were equal to 0.000346, 0.952, 7.02, and 0.00028, respectively. In addition, the proposed method analyzed the contribution rates of the rolling affecting parameters on the roll gap. The data showed that in the controllable rolling parameters, the rolling speed is the most impacting factor that disturbs the roll gap model in the acceleration and deceleration process, which can provide a useful direction for actual roll gap adjustment.
Abstract：Severe fluctuation of the effective roll gap in the acceleration and deceleration section of the cold rolling process is a significant factor causing thickness deviation. However, the conventional roll gap compensation method and control strategy do not meet the stringent strip quality requirements. The roll gap model in the acceleration and deceleration process is studied to increase the thickness control precision. In order to improve model accuracy, a roll gap prediction method based on data-driven is proposed. Given the complexities of the cold rolling process, the extreme gradient boosting (XGBoost) method is used to predict the roll gap model as the rolling speed changes. Meanwhile, support vector regression and neural network-based methods are taken to evaluate and compare the prediction performances. Based on the field data, the simulation experiments are carried out. It demonstrated that the prediction performance of the proposed method outperformed the other two methods. The values of root mean square error, determination coefficient value, mean absolute percentage error and mean absolute error obtained from the XGBoost model were equal to 0.000346, 0.952, 7.02, and 0.00028, respectively. In addition, the proposed method analyzed the contribution rates of the rolling affecting parameters on the roll gap. The data showed that in the controllable rolling parameters, the rolling speed is the most impacting factor that disturbs the roll gap model in the acceleration and deceleration process, which can provide a useful direction for actual roll gap adjustment.
Yun-jian Hu,Jie Sun,Huai-tao Shi, et al. Roll gap prediction in acceleration and deceleration process of cold rolling based on a data-driven method[J]. Journal of Iron and Steel Research International, 2023, 30(05): 1013-1021.
2023, Vol. 30 
