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Article type: Research Article
Authors: Tian, Xianghuaa | Luan, Fenga; * | Li, Xub; * | Wu, Yanc | Chen, Nanb
Affiliations: [a] School of Computer Science and Engineering, Northeastern University, Shenyang, China | [b] The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang, China | [c] School of Metallurgy, Northeastern University, Shenyang, China
Correspondence: [*] Corresponding author. Feng Luan, School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China. luanfeng@mail.neu.edu.cn. (F. Luan); Xu Li, The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China. E-mail: lixu@ral.neu.edu.cn. (X. Li)
Abstract: In the hot strip rolling process, accurate prediction of bending force is beneficial to improve the accuracy of strip crown and flatness, and further improve the strip shape quality. Due to outliers and noise are commonly present in the data generated in the rolling process, not only the prediction accuracy should be considered, but also the uncertainty of prediction results should be described quantitatively. Therefore, for the first time, the authors establish an interval prediction model for bending force in hot strip rolling process. In this paper, we use Artificial Neural Network (ANN) and whale optimization algorithm (WOA) to produce a prediction interval model (WOA-ANN) for bending force in hot strip rolling. Based on the point prediction by ANN, interval prediction is completed by using lower upper bound estimation (LUBE) and WOA, and three indexes are used to evaluate the performance of the model. This paper uses real world data from steel factory to determine the optimal network structure and parameters of the interval prediction model. Furthermore, the proposed WOA-ANN model is compared with other interval prediction models established by other three optimization algorithms. The experimental results show that the proposed WOA-ANN model has high reliability and narrow interval width, and can well complete the interval prediction of bending force in hot strip rolling. This study provides a more detailed and rigorous basis for setting bending force in hot strip rolling process.
Keywords: Artificial neural network (ANN), whale optimization algorithm (WOA), bending force, lower upper bound estimation (LUBE), interval prediction
DOI: 10.3233/JIFS-221338
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7297-7315, 2022
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