A deep learning model for the accurate prediction of the microstructure performance of hot rolled steel. (23rd September 2021)

• Record Type: Journal Article
• Authors: Wang, Bin-bin; Song, Yong; Wang, Jing
• Is Part Of: International journal of manufacturing research. Volume 16:Number 3(2021)
• Page Start: 262
• Page End: 279
• Publication Date: 2021-09-23
• Subjects: auto encoder -- deep learning -- hot rolled steel -- microstructure prediction -- steel properties; Manufacturing processes -- Periodicals; Manufacturing processes -- Automation -- Periodicals; Production engineering -- Periodicals; Factory management -- Periodicals; 670.5
• Languages: English
• ISSNs: 1750-0591

Abstract

The prediction of microstructure performance can guide the adjustment of parameters during hot rolling. Scholars from all over the world has developed physical metallurgical models of rolling process based on the physical and thermodynamic characteristics of strip steel, but the prediction accuracy of the model is greatly affected by the complex production environment. In recent years, neural network method is used to build the prediction model of organisational performance. However, the prediction accuracy and robustness of the single hidden layer neural network model are poor. Deep learning method is introduced in this paper to establish the prediction model of hot rolling microstructure performance in this paper. The application results show that compared with the traditional model, the prediction accuracy of the hot rolled steels yield strength, tensile strength and elongation increased by 3.46%, 2.35%, and 5.11%, respectively. [Submitted 13 March 2019; Accepted 27 October 2019]