Controlled Cooling Temperature Prediction of Hot-Rolled Steel Plate Based on Multi-Scale Convolutional Neural Network

Abstract

Controlled cooling technology is widely used in hot-rolled steel plate production lines. The final cooling temperature directly affects the microstructure and properties of steel plates, but cooling and heat transfer constitutes a nonlinear process, which is difficult to be accurately described using a mathematical model. In order to improve the accuracy of the controlled cooling temperature, a multi-scale convolutional neural network is used to predict the final cooling temperature. Convolution kernels with different sizes are introduced in the layer of a multi-scale convolutional neural network. This structure can simultaneously extract the feature information of different sizes and improve the perceptual power of the network model. The measured steel plate thickness, speed, header flow, and other variables are taken as input. The final cooling temperature is taken as the output and predicted using a multi-scale convolutional neural network. The results show that the multi-scale convolution neural network prediction model has strong generalization and nonlinear fitting ability. Compared with the traditionally structured BP neural network and convolution neural network (CNN), the mean square error (MSE) of the multi-scale convolutional neural network decreased by 24.7% and 12.2%, the mean absolute error (MAE) decreased by 19.6% and 7.97%, and the coefficient of determination (R2) improved by 4.26% and 2.65%, respectively. The final cooling temperature traditional structure by the multi-scale CNN agreed with the actual temperature within ±10% error bands. As the prediction accuracy improved, the multi-scale CNN can be effectively applied to hot-rolled steel plate production.