Prediction and analysis of key parameters of head deformation of hot-rolled plates based on artificial neural networks

In plate hot rolling, the prediction of head deformation of steel plates is crucial to improve the yield. In this paper, a novel prediction model based on neural networks for head deformation of medium-thick plates is proposed. The dataset of parameters of head deformation of medium-thick plates was established based on machine vision, and the artificial neural networks optimized by improved sparrow search algorithm (ISSA-ANN) was proposed to predict the irregular length and area of the head of finished medium-thick plates. During model parameters setting, mean error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R) are used as the evaluation indexes. Results show that ISSA-ANN performs better than other models in this paper in predicting the head deformation of medium-thick plates. Meanwhile, the effect of key variables on head deformation is investigated and suggestions for improving the head shape of medium-thick plates are given according to the influence of key variables of the model.