Crown Prediction of Hot‐Rolled Silicon Steel Using Transfer Learning Network Fused with Whale Optimization Algorithm

Abstract

Massive amounts of data are generated during hot strip production in the hot‐rolling metal forming process; the resultant dataset is sufficient for model learning in strip steel crown prediction. However, the data of high‐grade nonoriented silicon steel are limited, and the rolling process parameters differ, resulting in poor crown prediction. Herein, a model based on the whale optimization algorithm and transfer learning to predict the crowns of hot‐rolled high‐grade nonoriented silicon strip steel is presented. The model is composed of convolutional and linear layers. The whale optimization algorithm is used to optimize the hyperparameters and obtain an optimal model during pretraining. The model is then fine tuned based on insufficient silicon steel data to achieve model migration. The application results show that the correlation coefficient reaches 0.993, which is the highest prediction accuracy among the comparison models. Furthermore, the root mean square error is 1.14 μm, and the hit rate within 4.0 μm of the crown deviation reaches 99.502%. In addition, the influences of four parameters on the crown of the silicon steel strip are studied based on the response surfaces. The results indicate that the proposed model can efficiently predict silicon steel strip crowns.