摘要
针对镀锌产线SKP辊印不良问题,本文提出了一种数据驱动与工艺优化深度融合的协同控制方法。首先,通过大数据分析揭示了外板长周期生产与辊印缺陷的量化关联。进而,构建了基于深度神经网络的辊面状态预测模型,实现了对辊印缺陷的精准事前预警。同时,提出了“周期性辊面刷新”的生产节奏智能优化策略,并建立了集成高分辨率扫描与渐进加载的上机辊全生命周期精细化管理体系。应用结果表明,该方法使异常换辊率显著降低,有效保障了高端外板的稳定生产。本研究为轧制过程质量控制提供了从“经验驱动”到“数据智能驱动”的可行性新路径。
To address the issue of SKP roll mark defects in the galvanizing line,this paper proposes a collaborative control method that deeply integrates data-driven approaches with process optimization.First,big data analysis was employed to quantify the correlation between prolonged production runs of exterior panels and the occurrence of roll marks.Subsequently,a deep neural network(DNN)-based model was developed to predict roll surface condition,enabling accurate early warnings of potential defects.Furthermore,an intelligent production rhythm optimization strategy,termed“periodic roll surface refreshing,”was introduced.A refined lifecycle management system for work rolls was also established,incorporating high-resolution scanning and progressive loading protocols.Application results demonstrate that the proposed method significantly reduces the rate of abnormal roll changes,thereby ensuring the stable production of high-grade exterior panels.This research presents a paradigm shift for quality control in rolling processes,moving from an“experience-driven”to a“data-and-intelligence-driven”approach.
作者
杨逾
张康武
黄贯
曲格
秦陆宇
许朝阳
徐扬欢
YANG Yu;ZHANG Kangwu;HUANG Guan;QU Ge;QIN Luyu;XU Zhaoyang;XU Yanghuan(Guangzhou JFE Steel Sheet Co.,Ltd.,Guangzhou 511466,China;China National Heavy Machinery Research Institute Co.,Ltd.,Xi’an 710018,China;Xi’an Heavy Equipment&Technology Co.,Ltd.,Xi’an 710018,China;College of Mechanical Engineering,North China University of Science and Technology,Tangshan 063210,China)
出处
《重型机械》
2025年第6期22-27,共6页
Heavy Machinery
基金
河北省自然科学基金面上项目(E2023203065)。
关键词
镀锌
平整机
辊印
表面质量
换辊率
galvanizing line
temper mill
roll marks
surface quality
abnormal roll change rate
