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Open AccessArticle

A Comparative Assessment of Six Machine Learning Models for Prediction of Bending Force in Hot Strip Rolling Process

by Xu Li 1,*, Feng Luan 2,* and Yan Wu 3
1
The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China
2
School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
3
School of Metallurgy, Northeastern University, Shenyang 110819, China
*
Authors to whom correspondence should be addressed.
Metals 2020, 10(5), 685; https://doi.org/10.3390/met10050685
Received: 12 April 2020 / Revised: 15 May 2020 / Accepted: 20 May 2020 / Published: 22 May 2020
(This article belongs to the Special Issue Forming Processes of Modern Metallic Materials)
In the hot strip rolling (HSR) process, accurate prediction of bending force can improve the control accuracy of the strip crown and flatness, and further improve the strip shape quality. In this paper, six machine learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVR), Classification and Regression Tree (CART), Bagging Regression Tree (BRT), Least Absolute Shrinkage and Selection operator (LASSO), and Gaussian Process Regression (GPR), were applied to predict the bending force in the HSR process. A comparative experiment was carried out based on a real-life dataset, and the prediction performance of the six models was analyzed from prediction accuracy, stability, and computational cost. The prediction performance of the six models was assessed using three evaluation metrics of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results show that the GPR model is considered as the optimal model for bending force prediction with the best prediction accuracy, better stability, and acceptable computational cost. The prediction accuracy and stability of CART and ANN are slightly lower than that of GPR. Although BRT also shows a good combination of prediction accuracy and computational cost, the stability of BRT is the worst in the six models. SVM not only has poor prediction accuracy, but also has the highest computational cost while LASSO showed the worst prediction accuracy. View Full-Text
Keywords: bending force prediction; hot strip rolling (HSR); comparative assessment; machine learning; regression bending force prediction; hot strip rolling (HSR); comparative assessment; machine learning; regression
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MDPI and ACS Style

Li, X.; Luan, F.; Wu, Y. A Comparative Assessment of Six Machine Learning Models for Prediction of Bending Force in Hot Strip Rolling Process. Metals 2020, 10, 685.

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