Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.matdes.2020.109201
Title: Online prediction of mechanical properties of hot rolled steel plate using machine learning
Authors: Xie, Qian
Suvarna, Manu 
Li, Jiali 
Zhu, Xinzhe 
Cai, Jiajia
Wang, Xiaonan 
Keywords: Artificial neural networks
Machine learning
Mechanical properties prediction
Online prediction
Steel plate
Issue Date: 1-Jan-2021
Publisher: Elsevier Ltd
Citation: Xie, Qian, Suvarna, Manu, Li, Jiali, Zhu, Xinzhe, Cai, Jiajia, Wang, Xiaonan (2021-01-01). Online prediction of mechanical properties of hot rolled steel plate using machine learning. Materials and Design 197 : 109201. ScholarBank@NUS Repository. https://doi.org/10.1016/j.matdes.2020.109201
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: In industrial steel plate production, process parameters and steel grade composition significantly influence the microstructure and mechanical properties of the steel produced. But determining the exact relationship between process parameters and mechanical properties is a challenging process. This work aimed to devise a deep learning model, to predict mechanical properties of industrial steel plate including yield strength (YS), ultimate tensile strength (UTS), elongation (EL), and impact energy (Akv); based on the process parameters as well as composition of raw steel, and apply it online to a real steel manufacturing plant. An optimal deep neural network (DNN) model was formulated with 27 inputs parameters, 2 hidden layers each having 200 nodes and 4 output parameters (27 × 200 × 200 × 4) with an initial learning rate 0.0001, using Adam optimizer and subjected to Z pre-processing method, to yield an accurate model with R2 = 0.907. The tuned DNN model, had a root mean square error of 21.06 MPa, 16.67 MPa, 2.36%, and 39.33 J, and root mean square percentage error of 4.7%, 2.9%, 7.7%, and 16.2%, for YS, UTS, EL and Akv respectively. Through comparative analysis, it was found that the accuracy of DNN model was higher than other classic machine learning algorithms. To interpret the model assumptions and findings, several local linear models were devised and analyzed to establish the link between process parameters and mechanical properties. Finally the tuned DNN model was deployed in the real-steel plant for online monitoring and control of steel mechanical properties, and to guide the production of targeted steel plates with tailored mechanical properties. © 2020 The Authors
Source Title: Materials and Design
URI: https://scholarbank.nus.edu.sg/handle/10635/232181
ISSN: 0264-1275
DOI: 10.1016/j.matdes.2020.109201
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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