Transactions of Materials and Heat Treatment

Title：Rolling force prediction of hot strip rolling based on GA-PELM

Authors：

Unit：

KeyWords：

ClassificationCode：TP183

year,vol(issue):pagenumber：2022,47(1):43-48

Abstract：

In the process of hot strip rolling, the calculation accuracy of rolling force directly affects the actual thickness of strip steel, which is also the prerequisite of accurate online control. However, because the actual rolling process is affected by many factors, the prediction accuracy using the traditional model is often lower. Therefore, in order to improve the prediction accuracy of rolling force, the rolling force prediction model of parallel heterogeneous limit learning machine (PELM) was proposed, which had high learning speed and strong generalization ability, and at the same time, in order to ensure the stability of the model, the weight and deviation of the model was optimized by genetic algorithm (GA). Taking the actual production data of 2250 production line for a steel plant in Baotou as the example to predict the rolling force, the results show that the rolling force prediction model trained by the algorithm has good prediction accuracy and is suitable for the rolling force prediction of hot strip rolling process.

Funds：

国家自然科学基金资助项目（62063027）；内蒙古自然基金资助项目（2019MS06002）；内蒙古自治区研究生教育创新计划

作者简介：杨静（1996-），女，硕士研究生 E-mail：15191734@qq.com 通信作者：任彦（1977-），女，博士，教授 E-mail：1121996049@qq.com

Reference：

[1]Liu J, Liu X, Le B T. Rolling force prediction of hot rolling based on GA-MELM [J]. Complexity, 2019，(4):1-11.

[2]郝心耀.基于机器学习算法的轧机轧制力预测[J].现代电子技术,2016,39(20):114-116，120.

Hao X Y. Rolling mill rolling force prediction based on machine learning algorithm[J]. Modern Electronics Technique, 2016, 39(20):114-116，120.

[3]Zhang F, Zhao Y T, Shao J, et al. Rolling force prediction in heavy plate rolling based on uniform differential neural network[J]. Journal of Control Science & Engineering, 2016：1-9.

[4]刘明华,张强,刘英华,等.基于机器学习的热轧轧制力预测[J].锻压技术,2021, 46(10):233-241.

Liu M H, Zhang Q, Liu Y H, et al. Prediction of rolling force in hot rolling based on machine learning [J]. Forging ＆ Stamping Technology,2021, 46(10):233-241.

[5]冀秀梅,王龙,高克伟,等.极限学习机在中厚板轧制力预报中的应用[J]. 钢铁研究学报, 2020,32(5):393-399.

Ji X M, Wang L, Gao K W, et al. Application of ELM to predict plate rolling force [J]. Journal of Iron and Steel Research, 2020,32(5):393-399.

[6]Zheng G, Ge L H, Shi Y Q, et al. Dynamic rolling force prediction of reversible cold rolling mill based on BP neural network with improved PSO[A]. 2018 Chinese Automation Congress (CAC) [C]. 2018.

[7]Wang Z H, Gong D Y, Li X, et al. Prediction of bending force in the hot strip rolling process using artificial neural network and genetic algorithm (ANN-GA)[J]. International Journal of Advanced Manufacturing Technology, 2017, 93(4):1-14.

[8]Zhang Z K, Luan F, Li D, et al. Prediction of rolling force in the hot strip rolling using support vector regression with principal components analysis[A]. 2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE) [C]. IEEE, 2020.

[9]Yang Y B, Peng Y. Dynamic rolling model based on uniform deformation[J]. Journal of Manufacturing Processes, 2020, 58:1334-1347.

[10]彭文, 姬亚锋, 陈小睿,等. 热轧非稳态过程轧制力自学习模型优化[J]. 东北大学学报:自然科学版, 2019, 40(10):1408-1412.

Peng W, Ji Y F, Chen X R, et al. Optimization of rolling force self-learning model in unsteady process of hot rolling[J]. Journal of Northeastern University: Natural Science, 2019, 40(10):1408-1412.

[11]孙一康.冷热轧板带轧机的模型与控制[M].北京：冶金工业出版社，2010.

Sun Y K. Model and Control of Cold and Hot Strip Rolling Mill[M]. Beijing: Metallurgical Industry Press, 2010.

[12]曹建国，张杰，张少军.轧钢设备及自动控制[M]. 北京：化学工业出版社,2010.

Cao J G, Zhang J, Zhang S J. Rolling Equipment and Automatic Control[M]. Beijing: Chemical Industry Press, 2010.

[13]何飞,石露露,黎敏,等.基于多模态和加权支持向量机的热轧轧制力智能预报[J].工程科学学报,2015,37(4):517-521.

He F, Shi L L, Li M, et al. Intelligent prediction of rolling force in hot rolling based on a multi-model and weighted support vector machine[J]. Journal of Engineering Science, 2015, 37(4):517-521.

[14]洪悦,唐立新,张颜颜.基于数据子空间PLS建模技术的热轧轧制力优化设定[J].控制与决策,2014,29(7):1199-1204.

Hong Y, Tang L X, Zhang Y Y. Optimization of rolling force of hot rolling by using data subspace PLS modeling technique [J].Control and Decision, 2014,29(7):1199-1204.

[15]周富强,曹建国,张杰,等.冷连轧机轧制力在线计算模型[J].北京科技大学学报,2006,28(9):859-862.

Zhou F Q, Cao J G, Zhang J, et al. On-line calculation model of rolling force for tandem cold rolling mill [J]. Journal of University of Science and Technology Beijing, 2006,28(9): 859-862.

[16]宋勇,苏岚,荆丰伟,等.热轧带钢轧制力模型自学习算法优化[J].北京科技大学学报,2010,32(6):802-806.

Song Y, Su L, Jin F W, et al. Self-learning algorithm optimization for the rolling force model of hot strips[J]. Journal of University of Science and Technology Beijing, 2010, 32(6): 802-806.

[17]魏立新,翟博豪,赵志伟,等.基于半监督深度网络的冷连轧轧制力预报[J].塑性工程学报,2020,27(11):70-76.

Wei L X, Zhai B H, Zhao Z W, et al. Prediction of cold continuous rolling force based on semi-supervised deep network[J]. Journal of Plasticity Eengineering, 2020, 27(11): 70-76.

[18]陈丹,邵健,殷实,等.基于大数据平台的冷连轧轧制力自学习模型优化[J].冶金自动化,2020,44(6):25-29，61.

Chen D, Shao J, Yin S, et al. Optimization of self-learning model of cold rolling force based on big data platform[J]. Metallurgical Industry Automation, 2020, 44(6):25-29, 61.

[19]章顺虎,姜兴睿,尤凤翔,等.融合工业大数据的热轧厚板轧制力模型研究[J].精密成形工程,2020,12(2):8-14.

Zhang S H, Jiang X R, You F X, et al. Investigation on the model of rolling force by integrating industrial big data [J]. Journal of Netshape Forming Eengineering, 2020,12(2):8-14.

[20]孙全龙,梅益,杨幸雨.压铸模型腔曲面铣削表面粗糙度GA-ELM预测[J].机械设计与制造,2020,(8):188-191,196.

Sun Q L, Mei Y, Yang X Y. GA-ELM Prediction of surface roughness of die casting die cavity surface milling[J]. Machinery Design & Manufacture, 2020,(8):188-191,196.

[21]Wang Y Q, Dou Y, Liu X W, et al. PR-ELM: Parallel regularized extreme learning machine based on cluster[J]. Neurocomputing,2016, 173:1073-1081.

[22]陈则王,李福胜,林娅,等.基于GA-ELM的锂离子电池RUL间接预测方法[J].计量学报,2020,41(6):735-742.

Chen Z W, Li F S, Lin Y, et al. Indirect prediction method of rul for lithium-ion battery based on GA-ELM[J]. Acta Metrologica Sinica, 2020,41(6):735-742.

[23]马威,李维刚,赵云涛,等.基于深度学习的热连轧轧制力预测[J].钢铁研究学报,2019,31(9):805-815.

Ma W, Li W G, Zhao Y T, et al. Prediction of hot-rolled roll force based on deep learning[J]. Journal of Iron and Steel Research,2019,31(9):805-815.

Service：

【This site has not yet opened Download Service】【Add Favorite】