基于深度学习的热轧厚度-活套动态建模

doi:10.3901/JME.260181

摘要/Abstract

摘要： 热连轧控制过程中，厚度控制和活套控制相互耦合，严重影响了厚度控制系统的整体控制性能。传统建模方法难以准确描述带钢轧制过程中多变量耦合和非线性的复杂特性，为解决非线性系统的建模问题，针对某2 160 mm热连轧产线厚度控制过程为对象，基于生产实际数据建立了卷积-注意力机制-长短周期记忆预测模型(Convolution neural network-Attention-Long short term memory，CNN-ATT-LSTM)，并采用灰狼算法进行超参数优化；进一步与长短周期记忆网络模型(Long short term memory，LSTM)、深度神经网络模型(Deep neural network，DNN)和卷积网络-长短周期记忆网络模型(CNN-LSTM)进行预测效果对比。结果表明，所提出的预测模型的R²达到了93%，模型对厚度-活套系统的动态变化有更高的预测精度。最终选取三种不同规格的带钢，验证所提模型的泛化性能，表明该模型能够为厚度的高精度控制提供指导。

关键词: 热连轧, 厚度, 活套, 深度学习

Abstract: In the control process of hot strip rolling, the gague control and the looper control are interdependent and influence each other, which significantly impacts the overall performance of the gague control system. Traditional modeling methods struggle to accurately represent the intricate characteristics of multivariable coupling and nonlinearity present in the strip rolling process. To address the challenges in modelling nonlinear systems, focusing on a 2160 mm hot rolling line gague control process. A convolution-attention mechanism-long and short-term memory prediction model(CNN-ATT-LSTM) is established based on actual production data, and the hyper-parameters are optimized using the Gray Wolf algorithm. The prediction performance of this model is then compared with that of the long-short term memory network model(LSTM), the deep neural network model (DNN), and the convolutional network-long-short term memory network model(CNN-LSTM). The results indicate that the R² of the proposed prediction model reaches 93%, demonstrating a higher prediction accuracy for the dynamic changes within the gague-looper system. The generalization performance of the proposed model has been verified using three different strip sizes, demonstrating that the model is capable of providing guidance for high-precision control of gague.

Key words: hot rolling, gague, looper, deep learning

中图分类号:

TG335

雷佳为, 王姝婷, 岳重祥, 林萍, 彭文, 孙杰, 张殿华. 基于深度学习的热轧厚度-活套动态建模[J]. 机械工程学报, 2025, 62(6): 142-153.

LEI Jiawei, WANG Shuting, YUE Chongxiang, LIN Ping, PENG Wen, SUN Jie, ZHANG Dianhua. Deep Learning-based Dynamic Modeling of Hot Rolled Gague-looper System[J]. Journal of Mechanical Engineering, 2025, 62(6): 142-153.

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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260181

http://www.cjmenet.com.cn/CN/Y2025/V62/I6/142

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