Prediction of roll wear and thermal expansion based on informer network in hot rolling process and application in the control of crown and thickness

The inaccuracy of roll thermal expansion and roll wear settings affects strip steel plate shape and thickness in hot-rolling process. Roll wear and thermal expansion are nonlinear time series that cannot be neglected in the roll engineering. In order to make up for the shortcomings of the existing models, a novel prediction model of roll thermal expansion and roll wear based on Informer is developed. Firstly, multiple experiments are conducted to determine hyperparameter such as training epoch, dropout, batch size and learning rate. Then, the predicted results of finishing mill group (F1-F7 stand) are compared with the long-short-term memory (LSTM) model, the artificial neural network (ANN) model and the recurrent neural network (RNN) model. The results show that the model based on Informer network has highly accurate in predicting roll thermal expansion and roll wear. Finally, the application results show that the hit rate is 98.412 % within the tolerance range +/- 4 mu m and the hit rate within the +/- 40 mu m thickness tolerance deviation range reach 98.94 % which proves that the roll wear and thermal expansion model based on Informer has outstanding advantages in the hot rolling shape control system.