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, this paper first analyzes the main influencing factors of edge linear defects; then, based on modern 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.
收稿日期: 2020-07-09
出版日期: 2020-10-16
引用本文:
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]. , 2020, 55(11): 0-0.
Dong-cheng Yang-huan XU Hong-Min LIU. Data-driven Intelligent Prediction Model of Edge Seam Defects for Hot rolling strip. , 2020, 55(11): 0-0.
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2020, Vol. 55 
