Transactions of Materials and Heat Treatment

Title：Prediction on rolling force in hot rolling of wide and thick plate based on deep learning

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ClassificationCode：TG335.5+1

year,vol(issue):pagenumber：2022,47(7):167-174

Abstract：

In order to improve the prediction accuracy of rolling force in the hot rolling production process control for wide and thick plate, a deep learning network model integrating SIMS model was constructed to predict the rolling force of wide and thick plate in hot rolling. Then, by using the deep learning framework, a deep learning network model based on residual connection was constructed, which integrated the calculated values of SIMS model, calculated the gradient of loss function through error back propagation, and updated and optimized the weight parameters by using the optimization algorithm combining Mini-Batch and RMSProp. Furthermore, a shortcut was created to inject the earlier signals into the downstream network layers by using the residual connection to introduce a pure linear information carrying track, and the over fitting phenomenon of the model was suppressed by using the early-stopping mechanism and batch normalization and other strategies to improve the prediction accuracy of the model. Based on the above modeling method, the rolling data of wide and thick plate in hot rolling production line was modeled experimentally. The results show that taking the ratio of absolute value for relative error less than 5% in the test set as the evaluation index, compared with the traditional SIMS model, the deep learning network integrating SIMS model based on residual connection can achieve high-precision prediction of rolling force, and the prediction accuracy of the model is improved by an average value of 21.72%.

Funds：

基于数字化发展的制造业生态构建和路径研究（2022-XY-100）

作者简介：郭金涛(1996-)，男，硕士研究生 E-mail：kimtao@shu.edu.cn 通信作者：余建波（1982-），男，博士，教授 E-mail：jbyu@shu.edu.cn

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