重型机械Issue(6)：22-27,6.
数据驱动的镀锌线平整机辊印预测与控制方法研究
Data-driven prediction and control of roll marks in galvanizing line temper mills
摘要
Abstract
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.关键词
镀锌/平整机/辊印/表面质量/换辊率Key words
galvanizing line/temper mill/roll marks/surface quality/abnormal roll change rate分类
矿业与冶金引用本文复制引用
杨逾,张康武,黄贯,曲格,秦陆宇,许朝阳,徐扬欢..数据驱动的镀锌线平整机辊印预测与控制方法研究[J].重型机械,2025,(6):22-27,6.基金项目
河北省自然科学基金面上项目(E2023203065). （E2023203065）
