热连轧粗轧区FES宽展模型及其优化  

FES width spread model and its optimization for a rough rolling mill

作　　者：王爱丽[1] 杨荃[1] 何安瑞[1] 刘华强[1] 

机构地区：[1]北京科技大学冶金工程研究院,北京100083

出　　处：《北京科技大学学报》2010年第4期515-519,共5页Journal of University of Science and Technology Beijing

基　　金：国家支撑计划资助项目(No.2006BAE03A06)

摘　　要：为了满足某厂1580热连轧机宽度控制精度需求,提高宽展模型的广泛适用性,利用ANSYS/LS-DYNA有限元软件,对热轧粗轧区立轧--平轧过程进行了模拟.根据模拟数据,系统地分析了轧件宽度、厚度、轧辊直径、立辊侧压量和厚度压下量对"狗骨"宽展、自然宽展和绝对宽展的影响规律.利用模拟数据并结合现场数据构造了FES(finite element simulation)"狗骨"宽展模型和自然宽展模型,并建立了PSO-BP神经网络(粒子群BP神经网络).最后,FES宽展模型与PSO--BP神经网络相结合预报第1、3和5道次的宽展,其预报值与实测值误差在1mm以内的均达到了99%以上,达到了宽度控制的精度要求.In order to meet the need of width control precision for a 1580 hot strip mill and to improve the applicability of width spread models,the process of edge rolling-horizontal rolling in the rough rolling area was simulated by the finite element software ANSYS/LS-DYNA.The effects of width,thickness,roll diameter,reduction in width and reduction in thickness on ＇dog-bone＇ width spread,natural width spread and absolute width spread were studied.The FES models of ＇dog-bone＇ width spread and natural width spread were established with simulating data and field data,and the PSO-BP neural network was founded.At last,the FES width spread models were combined with the PSO-BP neural network to predict the width spread of the first,third and fifth pass.The percentage of the case that the error between predicted data and the measured values is less than 1 mm was more than 99%,and the width control precision was satisfied.

关 键 词：热连轧机 宽度控制 宽展模型 神经网络 

分 类 号：TG335.11[金属学及工艺—金属压力加工]