冷连轧弯辊力精度改进PSO-SVM预测及补偿分析  

Prediction and compensation analysis of improved PSO-SVM bending force accuracy in tandem cold rolling

作　　者：白跃辉[1] BAI Yuehui(College of Mechanical and Electrical Engineering,Hebi Vocational and Technical College,Hebi 458030,Henan China)

机构地区：[1]鹤壁职业技术学院机电工程学院,河南鹤壁458030

出　　处：《锻压装备与制造技术》2024年第2期132-135,共4页China Metalforming Equipment & Manufacturing Technology

摘　　要：为了进一步控制冷连轧弯辊力精度,构建改进PSO-SVM预测模型。利用包含压缩因子的粒子群算法完成支持向量机参数的更高效寻优处理,对回归轧制参数实施反归一化获得弯辊力模型。根据现场实际轧制结果完成预测模型的验证过程。研究结果表明:采用改进PSO-SVM模型获得的预测性能指标在上述优化方法中达到了最低,改进PSO-SVM模型具备最优预测效果。设置可靠补偿后大幅降低了AFC系统工作量,促进了带钢板形效率的显著提升。弯辊力补偿值形成了与弯辊力几乎相同的变化规律,具备优异预测性能。In order to further control the bending force accuracy of tandem cold rolling,an improved PSO-SVM prediction model was constructed.Particle swarm optimization(PSO)with compression factor is used to optimize the parameters of support vector machine(SVM)more efficiently,and the roll bending force model is obtained by reverse normalization of regression rolling parameters.The verification process of the prediction model is completed according to the actual rolling results on site.The results show that the improved PSO-SVM model has the lowest prediction performance index among the above optimization methods,and the improved PSO-SVM model has the best prediction effect.The installation of reliable compensation greatly reduces the workload of the AFC system and promotes a significant increase in the efficiency of the strip shape.The bending force compensation value is almost the same as the bending force,which has excellent prediction performance.

关 键 词：冷轧 板形控制 弯辊力 粒子群算法 支持向量机 

分 类 号：TG335.1[金属学及工艺—金属压力加工]