Digital Model of Automatic Plate Turning for Plate Mills Based on Machine Vision and Reinforcement Learning Algorithm

Abstract

Plate turning is an essential step in the plate rolling process. The traditional control mode relies on the manual observation of billets and mainly manual operation. Manual plate turning becomes an external disturbance of the automatic control system of plate mills, which reduces the reproducibility and accuracy of the rolling process. The automatic plate turning function is urgently needed to improve the control level of the rolling line. In this paper, based on the improved image processing algorithm, the position and angle information of the billet conversion process are detected in real time, and the real-time processing of detection data in a complex production environment is realized. Based on the change in the billet rotation angle in the actual plate turning process, a mathematical model is constructed to simulate the plate turning process. On this basis, the digital model and optimization algorithm for automatic plate turning based on reinforcement learning are established, and the automatic optimization of plate turning speed and accuracy is completed. The field application of data-driven plate turning systems replaces manual plate turning control. The plate turning angle detection error of the system is ≤2°. The average plate turning time of each billet is greatly shortened compared with manual plate turning mode, and the fastest time can be shortened by more than 1 s, which greatly improves the production efficiency and is of great significance for improving the automatic control level and digital upgrade of plate mills.