Prediction model for mechanical properties of hot-rolled strips by deep learning

Abstract

The prediction of the mechanical properties of hot-rolled strips is a very complex, highly dimensional and nonlinear problem, and the published models might lack reliability, practicability and generalization. Thus, a new model was proposed for predicting the mechanical properties of hot-rolled strips by deep learning. First, the one-dimensional numerical data were transformed into two-dimensional data for expressing the complex interaction between the influencing factors. Subsequently, a new convolutional network was proposed to establish the prediction model of tensile strength of hot-rolled strips, and an improved inception module was introduced into this network to abstract features from different scales. Many comparative experiments were carried out to find the optimal network structure and its hyperparameters. Finally, the prediction experiments were carried out on different models to evaluate the performance of the new convolutional network, which includes the stepwise regression, ridge regression, support vector machine, random forest, shallow neural network, Bayesian neural network, deep feed-forward network and improved LeNet-5 convolutional neural network. The results show that the proposed convolutional network has better prediction accuracy of the mechanical properties of hot-rolled strips compared with other models.