Research on edge defect prediction of hot-rolled strip steel based on LGBM-LR

Research on edge defect prediction of hot-rolled strip steel based on LGBM-LR | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on edge defect prediction of hot-rolled strip steel based on LGBM-LR Yunfei Liu, Jingbin Shao, Jianxin Tang, Weiliang Liu, Jianliang Sun, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6653536/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Surface quality control of hot-rolled strip remains challenging due to complex defect formation mechanisms and imbalanced datasets from industrial processes. To address this, we propose a predictive framework integrating Light Gradient Boosting Machine (LGBM) and Logistic Regression (LR) to identify defect-related features and enhance defect prediction. The model focuses on edge defects, leveraging historical manufacturing data for early-stage quality guidance. High-dimensional process data are analyzed using LGBM to rank defect-critical features, followed by LR-based interpretation to evaluate high/low-score variables. To address severe sample imbalance caused by scarce defective samples compared to normal samples, the framework integrates cross-stratified undersampling, BorderlineSMOTE oversampling, and adaptive LR threshold optimization. Comparative experiments are conducted using LGBM and hybrid LGBM-LR models with varied sampling techniques and decision thresholds. Results demonstrate that both models achieve 87% accuracy in defect anomaly prediction. The proposed BorderlineSMOTE and adaptive thresholding significantly improve recall rates from 20–60%, effectively addressing class imbalance. Furthermore, 13 of the top 20 features identified in the edge defect importance ranking align with empirical domain knowledge, validating the model's interpretability. This feature-ranking outcome offers actionable insights for prioritizing process parameters in surface quality control. The study highlights the efficacy of combining ensemble learning, resampling techniques, and threshold adjustment to enhance defect prediction in imbalanced industrial datasets, providing a data-driven reference for optimizing hot-rolled strip production. Hot-rolled strip Edge defect Anomaly prediction Light Gradient Boosting Machine Logistic Regressio Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6653536","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":461046458,"identity":"f39f555f-e58d-4c09-ab43-fdc43c195631","order_by":0,"name":"Yunfei Liu","email":"","orcid":"","institution":"National Key Laboratory of Metal Forming Technology and Heavy Equipment","correspondingAuthor":false,"prefix":"","firstName":"Yunfei","middleName":"","lastName":"Liu","suffix":""},{"id":461046459,"identity":"494c4586-1bb0-4775-ad33-580880e70b18","order_by":1,"name":"Jingbin Shao","email":"","orcid":"","institution":"National Engineering 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To address this, we propose a predictive framework integrating Light Gradient Boosting Machine (LGBM) and Logistic Regression (LR) to identify defect-related features and enhance defect prediction. The model focuses on edge defects, leveraging historical manufacturing data for early-stage quality guidance. High-dimensional process data are analyzed using LGBM to rank defect-critical features, followed by LR-based interpretation to evaluate high/low-score variables. To address severe sample imbalance caused by scarce defective samples compared to normal samples, the framework integrates cross-stratified undersampling, BorderlineSMOTE oversampling, and adaptive LR threshold optimization. Comparative experiments are conducted using LGBM and hybrid LGBM-LR models with varied sampling techniques and decision thresholds. Results demonstrate that both models achieve 87% accuracy in defect anomaly prediction. 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