基于有限元和PSO-BP法的20辊轧机轧制板形预测  

Shape Prediction of 20-High Rolling Mill Based on Finite Element and PSO-BP Method

作　　者：石坤[1] 王兴 郑岗[2] 刘云飞[3] 王大号[3] SHI Kun;WANG Xing;ZHENG Gang;LIU Yunfei;WANG Dahao(School of Mechanical and Precision Instrument Engineering,Xi’an University of Technology,Xi’an Shaanxi 710048,China;School of Automation and Information Engineering,Xi’an University of Technology,Xi’an Shaanxi 710048,China;China National Heavy Machinery Research Institute Co.,Ltd.,Xi’an Shaanxi 710000,China)

机构地区：[1]西安理工大学机械与精密仪器工程学院,陕西西安710048 [2]西安理工大学自动化与信息工程学院,陕西西安710048 [3]中国重型机械研究院股份公司,陕西西安710000

出　　处：《机床与液压》2023年第4期152-157,共6页Machine Tool & Hydraulics

基　　金：国家自然科学基金面上项目(11872300);陕西省重点研发计划项目(2017ZDXM-GY-133)。

摘　　要：针对20辊轧机轧制板形受到多重因素影响、难以精确预测的问题,基于有限元和PSO-BP法,建立20辊轧机轧制板形质量预测模型。根据20辊轧机轧辊间的位置关系,基于有限元软件ANSYS/LS-DYNA,考虑轧辊弹性变形、板带塑性变形与摩擦等因素,建立20辊轧机辊系有限元模型,分析板宽、厚度、张力、速度等因素对板形指数的影响;综合考虑不同轧制板形影响因素,以板形指数作为板形质量衡量指标,基于BP神经网络建立轧制板形质量预测模型,采用粒子群算法优化BP神经网络板形质量预测模型的权值和阈值,提高板形预测精度。In view of the fact that the rolling shape of the 20-high rolling mill is affected by multiple factors,it is difficult to accurately predict,based on finite element and PSO-BP method,a prediction model of the rolling shape quality of a 20-high rolling mill was established.According to the positional relationship between the rolls of the 20-high mill,based on the finite element software ANSYS/LS-DYNA,a finite element model of the roll system of a 20-high rolling mill was established,considering the factors such as roll elastic deformation,strip plastic deformation and friction.The influence of plate width,thickness,tension and speed on the shape index was analyzed.Considering the different influencing factors of rolling shape,the quality prediction model of rolling shape of a 20-high rolling mill was established based on BP neural network,in which the shape index was used as the measurement index of shape quality.The particle swarm optimization algorithm was used to optimize the weights and thresholds of the BP neural network of the shape quality prediction model,then the shape prediction accuracy is improved.

关 键 词：20辊轧机 轧制板形 有限元 PSO-BP神经网络 

分 类 号：TH164[机械工程—机械制造及自动化]