基于神经网络的热轧带钢宽度预报与设定

研究带钢热连轧生产线中成品带钢的宽度预报与设定.由于精轧道次带钢宽度变化与板坯化学成分、立辊侧压量、厚度压缩比、钢板温度、速度及张力等因素有关,所以在宽度预报中,按照轧制顺序将整个轧制过程分为两部分:狗骨轧制和随后的精轧道次,前者用数学机理模型建模,后者引入主成分分析-径向基函数(PCA-RBF)神经网络建模.应用效果表明,经过训练的神经网络模型能够有效提高带钢宽度的预报精度,减小成品带钢的宽度波动.

Hot Strip Width Prediction and Setup with Neural Networks

Investigates the width prediction and setup of strip in rolling passes in a hot strip mill. It is found that width variation after the finishing rolling is determined by factors like the chemical composition, edging draft, longitudinal ratio, strip temperature, strip speed, strip tension, and others. According to this the width prediction during the strip rolling is classified into two processes: the dog bone rolling and the succeding finishing rolling. The mathematical model is used for the former and the principal component analysis-radial basis function (PCA-RBF) networks for the latter. It is shown that a well-trained neural network model can improve the accuracy in width prediction. Based on the prediction model, width variations in the final strips are decreased.