Optimization strategy of bending force and rolling shift for hot rolling process of silicon steel based on stochastic configuration network-dung beetle optimization algorithm
Hot rolled silicon steel plate shape has significant genetic effect on the cold rolled plate shape and edge drop,and reducing the hot rolled silicon steel products transverse with the same plate difference can effectively improve the quality of cold rolled products.However,in the process of hot rolling of silicon steel,changing specifications or steel grades can lead to inaccurate pre-set values of bending force and rolling shift,which leads to a poor plate shape control effect.To address this prob-lem,this paper proposes a prediction model for silicon steel crown based on stochastic configuration network(SCN).To improve the model's capacity to fit the data,the number of hidden layers in the model was increased(DeepSCN),and incorporated the manifold regularization term in the SCN mod-eling process(RSC).Guided by the data-driven model's prediction results,the dung beetle optimiza-tion(DBO)algorithm was used to optimize the bending force and rolling shift.The results show that this method can control the fluctuation of the proportional crown within a small range,and the data of the finishing mill exit plate crown deviation within±5 μm can be improved to 92.2％.This not only effectively improves the quality of plate shape of silicon steel,but also provides a new research direc-tion and technical method for the control of plate shape in silicon steel.
silicon steel plate shapestochastic configuration network(SCN)manifold regulariza-tionoptimization of bending force and rolling shiftdung beetle optimization(DBO)
万方数据
维普
