Prediction model for mechanical properties of hot-rolled strips by deep learning
Wei‑gang Li1, Lu Xie1, Yun‑tao Zhao1, Zi‑xiang Li2, Wen‑bo Wang3
1 Engineering Research Center of Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China 2 Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China 3 Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China
Prediction model for mechanical properties of hot-rolled strips by deep learning
Wei‑gang Li1, Lu Xie1, Yun‑tao Zhao1, Zi‑xiang Li2, Wen‑bo Wang3
1 Engineering Research Center of Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China 2 Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China 3 Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China
摘要 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.
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.
Wei‑gang Li,Lu Xie,Yun‑tao Zhao, et al. Prediction model for mechanical properties of hot-rolled strips by deep learning[J]. Journal of Iron and Steel Research International, 2020, 27(9): 1045-1053.
2020, Vol. 27 
