A high-precision crown control strategy for hot-rolled electric steel using theoretical model-guided BO-CNN-BiLSTM framework

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The prediction accuracy of strip crown is low under complex industrial data environments to general machine learning models, i.e., lack of reasonable mechanism explanation and spatial dimension dependence, which will directly affect the product quality of hot-rolled electrical steel. Therefore, a high-precision crown prediction model is proposed for electrical steel in hot rolling based on a theoretical model-guided BO-CNN-BiLSTM (Bayesian optimization, Convolution Neural Network, and Bidirectional Long Short-term Memory) framework. The work roll wear model, thermal crown model, and the secondary deformation model of the strip between stands, based on the primary deformation by the loaded gap profile, were constructed. The mechanism parameters and measured parameters are integrated into a dataset as input feature variables. In the TG-BO-CNN-BiLSTM framework, the CNN-BiLSTM model, which can achieve spatial dimension dependence, was used to extract its feature component and sequentially predict the crown using the dataset, simultaneously, the BO module optimizes the hyperparameter of the CNN-BiLSTM model. The advantages of the proposed model are verified by adopting multiple evaluation indicators, which improves running speed and prediction accuracy. The effects of process parameters on the crown with typical upstream and downstream stands were comprehensively analyzed with the proposed model. A high-precision crown control strategy, combining the framework and influence law, for multi-stand and multi-method was proposed to obtain the high-precision crown. The control strategy applied in 1450 mm 4-high hot strip mills shows that the production performance of electrical steel is significantly improved.