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 ...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.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.U21A20117 and 52074085)the Liao Ning Revitalization Talents Program(XLYC1907065)+1 种基金Liao Ning Province Doctoral Initiation Fund(2022-BS-188)Open Research Fund from State Key Laboratory of Rolling and Automation(No.2021RALKFKT006).
文摘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.
