Article(id=1308544168528535992, tenantId=1146029695717560320, journalId=1268266715432370238, issueId=1308544152535658686, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1006-4613.2026.01.007, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1746806400000, revisedDateStr=2025-05-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1789910841352, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789910841352, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789910841352, creator=13701087609, updateTime=1789910841352, updator=13701087609, issue=Issue{id=1308544152535658686, tenantId=1146029695717560320, journalId=1268266715432370238, year='2026', volume='', issue='1', pageStart='1', pageEnd='71', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789910837537, creator='13701087609', updateTime=1789955219063, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308730302504198699, tenantId=1146029695717560320, journalId=1268266715432370238, issueId=1308544152535658686, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308730302508393004, tenantId=1146029695717560320, journalId=1268266715432370238, issueId=1308544152535658686, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=50, endPage=57, ext={EN=ArticleExt(id=1308544168759222713, articleId=1308544168528535992, tenantId=1146029695717560320, journalId=1268266715432370238, language=EN, title=Research on Wear Prediction for Surface Coating of Descaling Rollers Based on Machine Learning, columnId=1268934131263115797, journalTitle=Angang Technology, columnName=Research and Development, runingTitle=null, highlight=null, articleAbstract=

To enhance the accuracy of wear prediction for coating layers on descaling rolls in critical metallurgical components and improve high-pressure water descaling efficiency, the application of machine learning in predicting the wear of the surface coating of the descaling rollser was deeply studied. The principles and structures of the prevalent algorithms of the multiple linear regression, the X G Boost and the random forest wear prediction model were introduced respectively. A data set was established using production data to predict wear coating on the surface of the descaling rolls. The predicted results were compared longitudinally with the measured ones, and the prediction results of the three algorithms were compared horizontally. The random forest wear prediction model achieved the highest prediction accuracy rates of 90.4% and 95.9% at error tolerances of ±0.001 0 mm and ±0.001 5 mm, respectively. Among the four performance indicators, the model achieved the highest coefficient of determination, while exhibiting the lowest values for root mean square error, mean square error, and mean absolute error in the other three indicators. The random forest wear prediction model proved optimal for actual production, providing high-value guidance for the setting of the deformation and reengineering empowerment mechanism of descaling rolls, which were key metallurgical components.

, authors=Yiqin PENG, Wenjie SHI, Kang YANG, Rui GUAN, Xin'gang AI, authorsList=Yiqin PENG, Wenjie SHI, Kang YANG, Rui GUAN, Xin'gang AI, authorCompany=null, correspAuthors=Xin'gang AI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1308544169015075258, articleId=1308544168528535992, tenantId=1146029695717560320, journalId=1268266715432370238, language=CN, title=基于机器学习的除鳞辊表面涂镀层磨损预测研究, columnId=1268934131426693655, journalTitle=鞍钢技术, columnName=研究与开发, runingTitle=null, highlight=null, articleAbstract=

为提高冶金关键部件除鳞辊表面涂镀层磨损预测的准确性，提升高压水除鳞的效率，深入研究了机器学习在除鳞辊表面涂镀层磨损预测中的应用，分别介绍了多元线性回归磨损预测模型、X G Boost磨损预测模型以及随机森林磨损预测模型常用算法的原理以及结构。利用生产数据建立数据集对除鳞辊表面涂镀层磨损进行预测，并将预测结果和实测结果进行纵向比较，将三种算法的预测结果进行横向比较。结果表明，误差范围在±0.001 0 mm和±0.001 5 mm时，随机森林磨损预测模型预测的命中率最高，分别为90.4%和95.9%；四项性能指标中，该模型的决定分数数值最大，均方根误差、均方误差和平均绝对误差三项指标数值最小，随机森林磨损预测模型最适用于实际生产，可以给冶金关键部件除鳞辊的形性退化和再造赋能机制的设定提供较高的参考价值。

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彭意钦，博士生，辽宁科技大学冶金工程专业。E-mail: 869218044@qq.com

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彭意钦，博士生，辽宁科技大学冶金工程专业。E-mail: 869218044@qq.com

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