Thickness Prediction for Precision Rolling Exit Based on Time Domain Convolutional Network
As for the precision rolling process,a thickness prediction model was constructed for precision rolling exit by introducing a time domain convolutional network algorithm.The feature information of time-series data of the precision rolling process was extracted by using this time-domain convolutional network model,and the prediction performance of the precision rolling exit thickness was improved by optimizing the structure and parameters of the model.The simulation results of the actual steel dataset show that the proposed time-domain convolutional network algorithm,compared to traditional methods,has significant advantages in evaluation indicators,such as root mean square error,average absolute percentage error,and coefficient of determination,which can provide critical information for decision of on-site engineers.
strip steelhot rollingthickness predictiontime-domain convolutional networkprecision rolling processtime-series datafeature extractionroot mean square error
万方数据
维普
