Data-driven intelligent prediction model of edge seam defects for hot rolling strip
WANG Dong-cheng1,2, XU Yang-huan1, DUAN Bo-wei1, WANG Yong-mei1, LIU Hong-min1,2
1. National Engineering Research Center for Equipment and Technology of Cold Rolling Strip, Yanshan University, Qinhuangdao 066004, Hebei, China; 2. State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao 066004, Hebei, China
Abstract:Edge seam defects are easy to occur in hot rolling strip, which not only seriously affect the yield, but also may affect the downstream process of hot rolling. It is very difficult to establish an accurate mechanism prediction model because of the complex and changeable factors affecting the edge seam defects. Therefore, first, the main influencing factors of edge seam defects are analyzed; then, based on intelligent methods, the intelligent prediction models of edge seam defects based on logical regression and neural network are established respectively, and the accuracy and generalization ability of the two models are analyzed; finally, based on the neural network intelligent prediction model, the heating process parameters are optimized, which makes the defect rate and defect closure rate are significantly reduced. The research results in this paper have practical significance for improving the surface quality of hot rolling strip, and can be applied to similar rolling lines.
王东城, 徐扬欢, 段伯伟, 汪永梅, 刘宏民. 数据驱动的热轧带钢边部线状缺陷智能预报模型[J]. 钢铁, 2020, 55(11): 82-90.
WANG Dong-cheng, XU Yang-huan, DUAN Bo-wei, WANG Yong-mei, LIU Hong-min. Data-driven intelligent prediction model of edge seam defects for hot rolling strip[J]. Iron and Steel, 2020, 55(11): 82-90.
[1] 游慧超,王东城,杲通,等. IF钢热轧边部线状缺陷产生机理[J]. 重型机械,2019(6):16.(YOU Hui-chao,WANG Dong-cheng,GAO Tong,et al. Formulation mechanism of edge seam defects for hot rolling IF steel[J]. Heavy Machinery,2019(6):16.) [2] 胡学文,王东城,杲通,等. IF钢热轧边部线状缺陷治理措施[J]. 重型机械,2020(1):53.(HU Xue-wen,WANG Dong-cheng,GAO Tong,et al. Treatment measures of edge seam defects for hot rolling IF steel[J]. Heavy Machinery,2020(1):53.) [3] Kohno K. An explanation for the mechanism of edge seam defects occurring in heavy plate[J]. Tetsu-to-Hagane,1987,73:S1062. [4] Toshiki H,Yukihiro M,Hidenori E. Formulation mechanism of edge-seam defects on hot-rolled stainless steel[J]. Journal of the JSTP,2013,54(633):913. [5] Toshiki H,Yukihiro M,Hidenori E. Control technique for edge-seam defect on ferritic stainless-steel surface[J]. Journal of the JSTP,2013,54(633):918. [6] 张华伟,朱蔚林,张仁其,等. 利用SSP模块改善热轧带钢边部线状缺陷[J]. 冶金设备,2011(2):28.(ZHANG Hua-wei,ZHU Wei-lin,ZHANG Ren-qi,et al. Research of improving edge seam defect by changing SSP anvil in hot strip mill[J]. Metallurgical Equipment,2011(2):28.) [7] 邸洪双,王晓南,宁忠良. 热轧板带边部缺陷形成机理及研究现状[J]. 河南冶金,2008,16(3):1.(DI Hong-shuang,WANG Xiao-nan,NING Zhong-liang. Formation mechanism and research status of hot rolling strip edge defect[J]. Henan Metallurgy,2008,16(3):1.) [8] 张所全,焦四海,丁建华,等. 轧制过程边部线状缺陷形成机理研究[J]. 重庆理工大学学报,2018,32(4):107.(ZHANG Suo-quan,JIAO Si-hai,DING Jian-hua,et al. Study on mechanism of edge seam defect during rolling process[J]. Journal of Chongqing University of Technology,2018,32(4):107.) [9] 曹建新,陶红标,张慧,等. 倒角结晶器在涟钢板坯连铸生产中的应用[J]. 钢铁,2013,48(11):43.(CAO Jian-xin,TAO Hong-biao,ZHANG Hui,et al. Application of chamfered mould on slab continuous casting production in lianyuan iron and steel company[J]. Iron and Steel,2013,48(11):43.) [10] ZHANG Hui,HU Peng,WANG Ming-lin. Mechanism and improvement of straight edge seam defect on hot-rolled plate surface through use of chamfered slabs[J]. Journal of Iron and Steel Research,International,2016,23(6):539. [11] 王国栋,朱鲁玲,张作贵,等. 热轧钢板表面翘皮缺陷分析[J]. 物理测试,2009,27(1):46.(WANG Guo-dong,ZHU Lu-ling,ZHANG Zuo-gui,et al. Analysis on surface upwarp defects in hot rolled steel plate[J]. Physics Examination and Testing,2009,27(1):46.) [12] 陈书浩,王新华,黄福祥,等. 热轧钢板表面翘皮缺陷的特征和形成机理[J]. 特殊钢,2011,32(5):47.(CHEN Shu-hao,WANG Xin-hua,HUANG Fu-xiang,et al. Characteristics and formation mechanism of surface upwarp defects of hot rolled plate[J]. Special Steel,2011,32(5):47.) [13] 李新创,栾治伟,施灿涛,等. 人工智能技术在钢铁行业中的应用研究[J]. 冶金自动化,2020,44(1):1.(LI Xin-chuang,LUAN Zhi-wei,SHI Can-tao,et al. Application of artificial intelligence in iron and steel industry[J]. Metallurgical Industry Automation,2020,44(1):1.) [14] 王国栋. 高质量中厚板生产关键共性技术研发现状和前景[J]. 轧钢,2019,36(1):7.(WANG Guo-dong. Status and prospects of research and development of key common technologies for high quality heavy and medium plate production[J]. Steel Rolling,2019,36(1):7.) [15] 刘文仲. 中国钢铁工业智能制造现状及思考[J]. 中国冶金,2020,30(6):1.(LIU Wen-zhong. Current situation and thinking of intelligent manufacturing in China's iron and steel industry[J]. China Metallurgy,2020,30(6):1.) [16] 刘鸿. 常用人工智能技术在钢铁领域中的应用概述[J]. 冶金自动化,2019,43(4):24.(LIU Hong. Survey for application of conventional artificial intelligence technologies in the steel iron field[J]. Metallurgical Industry Automation,2019,43(4):24.) [17] 颉建新,张福明. 钢铁制造流程智能制造与智能设计[J]. 中国冶金,2019,29(2):1.(XIE Jian-xin,ZHANG Fu-ming. Intelligent manufacturing and intelligent design of iron and steel manufacturing process[J]. China Metallurgy,2019,29(2):1.) [18] 王萌. 基于PKPCA和逻辑回归模型的滚动轴承寿命预测研究[D]. 大连:大连理工大学,2018.(WANG Meng. Bearing Performance Degradation Assessment Based on PKPCA and Logistic Regression Model[D]. Dalian:Dalian University of Technology,2018.) [19] 陈超,王楠,于海洋,等. 基于卡方分箱法和逻辑回归算法的转炉操作工艺评价模型[J]. 材料与冶金学报,2019,18(2):87.(CHEN Chao,WANG Nan,YU Hai-yang,et al. Evaluation model of converter operation process based on chi-square boxing method and logistic regression algorithm[J]. Journal of Materials and metallurgy,2019,18(2):87.) [20] 李威. 基于机器学习的森林多源遥感数据分析方法研究[D]. 哈尔滨:哈尔滨工程大学,2018.(LI Wei. Research on Forest Remote Sensing Data Analysis Methodology Based on Machine Learning[D]. Harbin:Harbin Engineering University,2018.) [21] 董学辉. 逻辑回归算法及其GPU并行实现研究[D]. 哈尔滨:哈尔滨工业大学,2016.(DONG Xue-hui. Research on Logistic Regression and Its Parallel Implementation on GPU[D]. Harbin:Harbin Institute of Technology,2016.) [22] 杨海龙,田莹,王澧冰. 基于优化损失函数的YOLOv2目标检测器[J]. 辽宁科技大学学报,2020,43(1):52.(YANG Hai-long,TIAN Ying,WANG Li-bing. Object detector YOLOv2 based on optimized loss function[J]. Journal of University of Science and Technology Liaoning,2020,43(1):52.) [23] 宋明明,王东城,张帅,等. 基于循环神经网络的板形模式识别模型[J]. 钢铁,2018,53(11):56.(SONG Ming-ming,WANG Dong-cheng,ZHANG Shuai,et al. Flatness pattern recognition model based on recurrent neural network[J]. Iron and Steel,2018,53(11):56.) [24] 徐化岩,马家琳. 基于数据驱动的高炉煤气复合预测模型[J]. 中国冶金,2019,29(7):56.(XU Hua-yan,MA Jia-lin. Composite prediction model of blast furnace gas based on data driven[J]. China Metallurgy,2019,29(7):56.) [25] 张秀玲,刘宏民. 板形模式识别的GA-BP模型和改进的最小二乘法[J]. 钢铁,2003,38(10):29.(ZHANG Xiu-ling,LIU Hong-min. GA-BP model of flatness pattern recognition and improved least-squares method[J]. Iron and Steel,2003,38(10):29.) [26] 裴洪,胡昌华,司小胜,等. 基于机器学习的设备剩余寿命预测方法综述[J]. 机械工程学报,2019,55(8):1.(PEI Hong,HU Chang-hua,SI Xiao-sheng,et al. Review of machine learning based remaining useful life prediction methods for equipment[J]. Journal of Mechanical Engineering,2019,55(8):1.)