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.
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2020, Vol. 55 
