Quantitative study on the relationship between the transverse thickness difference of cold-rolled silicon strip and incoming section profile based on the mechanism-intelligent model
Dongcheng Wang, Yanghuan Xu, Tongyuan Zhang, Xiaobao Ma, Hongmin Liu
Abstract
Cold-rolled non-oriented silicon strip is widely used, and users have strict requirements for its transverse thickness difference. It is of great significance to study the quantitative relationship between the transverse thickness difference and incoming section profile of cold-rolled silicon strip and to formulate appropriate control indexes of the hot-rolled profile. To achieve the above purpose, this paper first proposes a method to describe the section profile of hot-rolled strip. A mechanism model for predicting the transverse thickness difference of cold-rolled silicon strip is established. Based on the characteristics of neural network transfer learning, the calculated results of the mechanism model are combined with actual production data, and the PSO-LM-BP neural network is trained by using the strategy of pre-training + retraining to obtain the mechanism-intelligence model for the prediction of the transverse thickness difference of cold-rolled silicon strip. The innovation of this paper is the combination of physical model and neural network. The prediction accuracy of the model is improved by two orders of magnitude on average, and the operation time is reduced. The relationship between the hot-rolled strip section crown, wedge and cold-rolled strip transverse thickness difference is quantitatively analysed, and the control strategy diagram of the key parameters of the hot-rolled section is finally obtained. The production of cold-rolled silicon strip with 1420 mm UCM shows that this strategy has a beneficial effect on the transverse thickness difference control of a cold-rolled strip.