The Prediction Model for Transverse Thickness Difference of Electric Steel in 6‐High Cold Rolling Mills Based on GA‐PSO‐SVR Approach
Chunning Song, Jianguo Cao, Leilei Wang, Jing Xiao, Qiufang Zhao
Abstract
To meet the shape quality requirements of “Dead flat” rectangular section of electrical steel in the cold rolling process, the transverse thickness difference (TTD) prediction model of 6‐high tandem cold rolling mills (TCMs) based on genetic algorithm, particle swarm optimization, and support vector regression (GA‐PSO‐SVR) is proposed. The TTD prediction model uses 5000 coils of data obtained from a cold rolling line. The GA‐PSO‐SVR model is obtained by the GA‐PSO hybrid algorithm to search and obtain the optimal parameter of the SVR to improve the prediction model accuracy. The results reveal that using multiple evaluation indicators the GA‐PSO‐SVR model has preferably adaptability and higher accuracy. Meanwhile, the relative importance of input variables is calculated based on the GA‐PSO‐SVR model, which indicates that the shape control methods in stands 1–5 have the most important influence on the TTD. The TTD prediction model is continuously applied to 1420 mm 6‐high TCMs; the results show that the rate of the TTD less than 7 μm increased from 29.6% to 63.85%.