DONG Hao, WU Zhaosong, SUN Jie
Control Engineering of China. 2026, 33(2): 336-342.
Plate crown is the most important quality index to evaluate the cross-section profile of hot-rolled plate, and good plate crown is the guarantee of normal production, so accurate prediction of bad crown is of crucial significance to ensure production. In the actual production of hot rolling, the number of qualified crown samples is much higher than the undesirable crown, and a large number of nonlinear parameters and strong coupling between parameters make plate crown prediction a very complex imbalanced classification problem. According to the complex data characteristics of hot-rolled plate crown, considering the powerful nonlinear fitting ability of deep learning, combined with cost-sensitive learning to improve the misclassification cost of bad crown, a cost-sensitive deep belief network (CS-DBN) model is proposed. Evaluation indicators such as Macro-F1, Micro-F1, G-Mean and Aacc are used as evaluation indicators of the model. By adjusting the model hyperparameters and optimizer to determine the optimal cost-sensitive deep belief network, and comparing and analyzing it with traditional machine learning algorithms ANN, SVC, KNN, DBN, LR, the results show that CS-DBN is better than traditional machine learning models in all evaluation indicators, and the plate crown prediction results are good.
