数据驱动的热轧带钢边部线状缺陷智能预报模型

doi:10.13228/j.boyuan.issn0449-749x.20200353

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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.

Key words： data-driven hot rolling strip edge seam defect intelligent prediction model

Received: 09 July 2020

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	Articles by authors
	WANG Dong-cheng
	XU Yang-huan
	DUAN Bo-wei
	WANG Yong-mei
	LIU Hong-min

Cite this article:

WANG Dong-cheng,XU Yang-huan,DUAN Bo-wei等. 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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http://www.chinamet.cn/Jweb_gt/EN/10.13228/j.boyuan.issn0449-749x.20200353 OR http://www.chinamet.cn/Jweb_gt/EN/Y2020/V55/I11/82

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