Flatness prediction method based on operating mode recognition for roller quenching process

被引：0
|
作者：
Chen C. [1 ,2 ,3 ]
Wu M. [1 ,2 ,3 ]
Chen L.-F. [1 ,2 ,3 ]
Zhang W. [1 ,2 ,3 ]
Du S. [1 ,2 ,3 ]
机构：
[1] School of Automation, China University of Geosciences, Wuhan
[2] Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan
[3] Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2021年 / 38卷 / 09期
关键词：
Flatness prediction; Operating mode recognition; Particle swarm optimization algorithm; Roller quenching; Support vector machine;
D O I：
10.7641/CTA.2021.00626
中图分类号：
学科分类号：
摘要：
Flatness is an important indicator to measure the quality of quenched steel plate, and the prediction of flatness is of great significance for the continuous and stable production of high-quality steel plate. This paper proposes a method based on operating mode recognition to predict the flatness for the roller quenching process, which provides a reference for the quenching production control decision. Firstly, the characteristics of the quenching process are analyzed. Then the fuzzy C-means clustering algorithm is used to recognize the operating modes of the process, the support vector machine is used to establish the flatness prediction model for each operating mode, and the improved particle swarm optimization algorithm is applied to improve the accuracy of the model. Finally, experiments are performed using industrial production data, and the results verify the feasibility and effectiveness of the flatness prediction method proposed in this paper. © 2021, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：1407 / 1413
页数：6
相关论文
共 19 条
  • [1] BAI Zhenhua, ZHAO Weiquan, ZHANG Miaoxing, Et al., A shape prediction model of eight-high rolling mills and its applications, China Mechanical Engineering, 29, 4, pp. 493-498, (2018)
  • [2] WANG D C, LIU H M., A model coupling method for shape prediction, Journal of Iron and Steel Research International, 19, 2, pp. 22-27, (2012)
  • [3] LIU Jianchang, CHEN Yingying, ZHANG Ruiyou, Intelligent flatness-controller based on PSO-BP network, Control Theory & Applications, 24, 4, pp. 674-678, (2007)
  • [4] WANG Z H, MA G S, GONG D Y, Et al., Application of mind evolutionary algorithm and artificial neural networks for prediction of profile and flatness in hot strip rolling process, Neural Processing Letters, 50, pp. 2455-2479, (2019)
  • [5] RATH S, SENGUPTA P P, SINGH A P, Et al., Mathematical-artificial neural network hybrid model to predict roll force during hot rolling of steel, International Journal of Computational Materials Science and Engineering, 2, 1, pp. 1-16, (2013)
  • [6] ZHANG X L, GAO W Y, LAI Y J, Et al., Flatness predictive model based on T-S cloud reasoning network implemented by DSP, Journal of Central South University, 24, 10, pp. 2222-2230, (2017)
  • [7] DU S, MU M, CHEN L F, Et al., Operating mode recognition based on fluctuation interval prediction for iron ore sintering process, IEEE/ASME Transactions on Mechatronics, 25, 5, pp. 2297-2308, (2020)
  • [8] HU J, WU M, CHEN X, Et al., A multi-level prediction model of carbon efficiency based on differential evolution algorithm for iron ore sintering process, IEEE Transactions on Industrial Electronics, 65, 11, pp. 8778-8787, (2018)
  • [9] YUAN Guo, HAN Yi, WANG Chao, Et al., Cooling mechanism during quenching by plate roller quenching machine, Transactions of Materials and Heat Treatment, 31, 12, pp. 148-152, (2010)
  • [10] HAN Y., Study on temperature field and shape control for plate roller quenching machine, (2008)
12