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Prediction Model of Rolling Mill Stiffness based on Particle Swarm Optimization-Back Propagation (PSO-BP) Neural Network
编号：65 访问权限：仅限参会人 更新：2025-11-10 11:33:13 浏览：11次 口头报告

报告开始：2025年11月23日 10:50（Asia/Shanghai）

报告时间：20min

所在会场：[S2] Parallel Session 2 [S2-2] Parallel Session 2-23 AM

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摘要
The stiffness of the rolling mill roll system plays a crucial role in determining the shape of the hot rolled strip steel plate. It is directly influenced by the spatial position of the rolling mill roll system. This study establishes a numerical model of rolling mill stiffness through the spatial position of the roll system, and validates its accuracy using field measured data. Through the developed model, relevant data of the cross state and stiffness of the roll system are obtained. Focusing on the hot rolling mill, we propose a deep neural network (DNN) model called particle swarm optimization back propagation neural network (PSO-BP). The particle swarm optimization (PSO) algorithm is improved based on practical experience and simulation analysis: an adjustment factor α  is introduced to enforce physical constraints (influence weight of backup roll > work roll, drive side > operating side). Additionally, a nonlinear mapping mathematical model is established to quantify the relationship between the cross state of the roll system axis induced by the wear of the mill stand liner, and the mill stiffness. Our results demonstrate that the PSO-optimized BP model has higher prediction accuracy than conventional BP, genetic algorithm-BP (GA-BP), and PSO-BP without the adjustment factor.
关键词
Mill stiffness, Prediction model, Support vector regression, Finite element analysis
报告人
Qiu Bitao
‌Professor of engineer Wuhan University of Science and Technology

稿件作者
Qiu Bitao Wuhan University of Science and Technology
Dan Binbin Wuhan University of Science and Technology
Ruan Jinhua Wuhan University of Science and Technology
Guo Wanfu Wuhan University of Science and Technology
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重要日期

  • 会议日期

    11月21日

    2025

    至

    11月23日

    2025

  • 10月20日 2025

    初稿截稿日期

  • 11月23日 2025

    注册截止日期

主办单位
IEEE Instrumentation and Measurement Society
South China University of Technology
承办单位
South China University of Technology
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