Mechanical Properties Prediction for Hot Rolled Alloy Steel Using Convolutional Neural Network

Abstract

A convolutional neural network (CNN)-based method for predicting the mechanical properties of hot rolled steel using chemical composition and process parameters is proposed. The novel contribution of this research is to introduce the prediction method of CNN into the steel properties prediction field by converting the production data into two-dimensional data images. Compared with the traditional artificial neural network method, the CNN adopts the idea of local connection and weight sharing, which reduces the complexity of the network model, and uses convolution and pooling operations to extract local features for better prediction precision. The experiments in this paper show that the proposed CNN model with the optimal structure provides higher prediction accuracy and higher robustness when compared with other prediction model reported in the literature. Finally, the metallurgical phenomena in the steel rolling processes are verified by sensitivity analysis using the proposed CNN model. The results are consistent with the metallurgical properties of the steel materials used in the experiments. Therefore, the proposed CNN model has a guiding significance in predicting the mechanical properties of hot rolled steel products in practical applications.

Keywords

• Convolutional neural network
• mechanical property prediction
• deep learning
• hot rolled alloy steel