Prediction of strip steel product quality by combining small disturbance theory with INGO-GRNN

Prediction of strip steel product quality by combining small disturbance theory with INGO-GRNN | 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 Prediction of strip steel product quality by combining small disturbance theory with INGO-GRNN Jianliang Sun, Boyao Fang, Ziyi Wu, Lizhi Wang, Weiliang Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7892197/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract The quality control precision of strip rolling products is a crucial factor influencing the economic benefit and international com petitiveness of iron and steel enterprises. As the significant quality evaluation index of strip products, the precise control and measurement of thickness and flatness are of paramount importance in order to enhance the yield and use value of strip products. In order to address the issue of the limited fit degree between the current rolling mechanism model and machine learning algo rithm, a new algorithm based on Small Disturbance Theory (SDT) and Generalized Regression Neural Network (GRNN) has been proposed. The two-stage model framework of GRNN is used to predict the flatness and thickness quality of strip rolled products. Given the nonlinearity of the variables in actual rolling production, the small disturbance theory was introduced to linearize the deviation of the mechanism model caused by the perturbation amount and the adjustment amount. A linear error correction model with rolling force as the intermediate variable was established. The maximal relevance and minimal redundancy (mRMR) principle was employed to evaluate the importance of the rolling process feature parameters, and the feature subset of quality prediction with the most sufficient information and minimum data dimension was selected. Subsequently, the Improved Northern Goshawk Optimization (INGO) algorithm was employed to optimize the initial population distribution, after which the hyperparameters of the GRNN were optimized. This resulted in the establishment of a nonlinear error correction model with the prediction of flatness and thickness. To verify the predictive performance of the SDT-INGO-GRNN model, multiple indexes were calculated. The results demonstrate that the SDT-INGO GRNN model proposed in this paper exhibits accurate and stable prediction performance in the product quality prediction of the 1580 four-high hot tandem rolling line. The thickness prediction error is largely within 2 µm , and the flatness prediction error is largely within 0.5 I . Plate and strip rolling Quality prediction Strip flatness Small disturbance theory Generalized regression neural networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 19 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviews received at journal 11 Jan, 2026 Reviewers agreed at journal 30 Dec, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers invited by journal 25 Oct, 2025 Editor assigned by journal 23 Oct, 2025 Submission checks completed at journal 23 Oct, 2025 First submitted to journal 18 Oct, 2025 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. 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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-7892197","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539533603,"identity":"0e6c4d5a-19ab-45a3-a2db-377777d1af7f","order_by":0,"name":"Jianliang 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As the significant quality evaluation index of strip products, the precise control and measurement of thickness and flatness are of paramount importance in order to enhance the yield and use value of strip products. In order to address the issue of the limited fit degree between the current rolling mechanism model and machine learning algo rithm, a new algorithm based on Small Disturbance Theory (SDT) and Generalized Regression Neural Network (GRNN) has been proposed. The two-stage model framework of GRNN is used to predict the flatness and thickness quality of strip rolled products. Given the nonlinearity of the variables in actual rolling production, the small disturbance theory was introduced to linearize the deviation of the mechanism model caused by the perturbation amount and the adjustment amount. A linear error correction model with rolling force as the intermediate variable was established. The maximal relevance and minimal redundancy (mRMR) principle was employed to evaluate the importance of the rolling process feature parameters, and the feature subset of quality prediction with the most sufficient information and minimum data dimension was selected. Subsequently, the Improved Northern Goshawk Optimization (INGO) algorithm was employed to optimize the initial population distribution, after which the hyperparameters of the GRNN were optimized. This resulted in the establishment of a nonlinear error correction model with the prediction of flatness and thickness. To verify the predictive performance of the SDT-INGO-GRNN model, multiple indexes were calculated. The results demonstrate that the SDT-INGO GRNN model proposed in this paper exhibits accurate and stable prediction performance in the product quality prediction of the 1580 four-high hot tandem rolling line. 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