Combined prediction of hot strip crowns of hot tandem rolling based on mechanism and data driving

Abstract:

To address defects of the traditional method for predicting the strip outlet crown of hot tandem rolling, which suffers from low accuracy and poor interpretability, a model for combined prediction of hot strip crowns based on mechanism and data driving is proposed. The strip crown reference value is obtained using the strip crown mechanism prediction model. The deviation between the reference value and the actual value is used as the prediction variable of machine learning models, and then the deviation prediction value and the reference value are summed to obtain the strip crown prediction value of the combined prediction model. This combined prediction strategy is verified using multiple neural networks. It is found that the proposed strip crown combined prediction model has better prediction performance than the traditional model, with over 97% of the predicted data having an absolute error of less than 0.02 mm and more than 82% of the predicted data showing an absolute error of less than 0.01 mm. Additionally, the model is both satisfactorily feasible and widely applicable. The proposed model integrates the relative strengths of the mechanism model and the data-driven model, resulting in a representation that is more closely aligned with the actual physical phenomena. The combined model not only alleviates the problems of poor interpretation and low reliability with the results from the black-box neural network prediction, but also compensates for the defects of the mechanism model, which often produces results that deviate from the production conditions and cannot be adjusted in real time. This proposed model makes a significant contribution to the shape control of hot strip and the improvement of hot strip product quality.