Novel shape control system of hot-rolled strip based on machine learning fused mechanism model

Accurate prediction of strip shape and reasonable allocation of actuators in the finishing mill group are essential to improving the precision of shape control. The poor prediction of the traditional mechanism model and unreasonable allocation of rolling parameters lead to various shape defects. Thus, this study developed an intelligent shape control system to enhance shape control precision, establishing a model to predict strip shape and optimize the rolling parameters for the rolling actuators in the finishing mill. A prediction model based on the deep stochastic configuration network fusion mechanism (DSCNM) model was developed to predict the hotrolled strip shape, using ridge regression to supplement the features not provided by the mechanism model. A deep stochastic configuration network is used to approximate the errors between measured values and values computed by the mechanism model. The results show that the proposed fusion model is superior to current methods for strip shape prediction. The new model reduces prediction errors of the traditional mechanism by 62.23 %, supplying a credible foundation to optimize rolling parameters for shape control. In the optimization and allocation of rolling parameters, the rolling parameters of a finishing mill group are optimized by a sparrow search algorithm, which constructs a fitness function from the DSCNM and the target crown. A practical application of the proposed method produced a hot-rolled strip with 53.84 % fewer shape defects than the traditional method. Thus, the proposed method achieves superior quality, yield, and production cost.