Thickness Prediction for Precision Rolling Exit Based on Time Domain Convolutional Network
YANG Pingping1, MA Liang2
1.School of Advanced Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2.School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Abstract：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.
杨萍萍, 马亮. 基于时域卷积网络的精轧出口厚度预测[J]. 矿冶工程, 2024, 44(1): 138-142.
YANG Pingping, MA Liang. Thickness Prediction for Precision Rolling Exit Based on Time Domain Convolutional Network. Mining and Metallurgical Engineering, 2024, 44(1): 138-142.
2024, Vol. 44 
