Intelligent Optimization Control of Plate Plan-View Pattern Based on Intermediate Slab Pattern Vision Inspection and...

In the production process of plate, the main factors affecting the yield of plate are the crop-cutting and edge losses. It is very important to accurately predict the crop pattern of plates and effectively control the plan-view pattern. In this paper, a detection scheme is proposed to obtain the plan-view pattern of the intermediate slab and finished plate by placing detection devices after the roughing mill and finishing mill, respectively. An image processing algorithm is used to obtain a dataset of the plan-view pattern parameters, and a plan-view pattern prediction and control model for plate is established based on the BWO-DNN (beluga whale optimization-deep neural network) algorithm. The BWO algorithm is used to optimize the hyperparameters in the DNN algorithm to complete the establishment of the intelligent model. In terms of model analysis, goodness of fit (R²) and mean absolute error (MAE) are used as evaluation indicators. The results show that the intelligence model established based on BWO-DNN has good predictive and control performance, realizing intelligent prediction of the crop pattern of plates and parameter optimization of plan-view pattern control. The actual production verification on site shows that the irregular areas of the plate head and tail can be reduced by 17.2% and 22.6%, respectively.