Composite rectification in modeling of rolling force for hot rolled thick plate

Composite rectification in modeling of rolling force for hot rolled thick plate | 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 Composite rectification in modeling of rolling force for hot rolled thick plate Xiao Xiao Guo, Shun Hu Zhang, Li Wang, Wei Gang Li, Lei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3737740/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 In order to eliminate the predicted bias of the traditional Sims model, a new method, called the composite rectification method, is firstly proposed. Firstly, the deformation resistance model based on industrial big data is built based on the generalized additive principle. This new model is adopted to replace the deformation resistance model in the Sims model. Through this factor replacement, the effect of deformation resistance bias due to the traditional regression method was eliminated, resulting in the once-time corrected Sims model with significantly improved accuracy compared to the original Sims model. On this basis, to solve the mathematical form imperfection caused by the introduction of many assumptions during the derivation of the Sims model, a back propagation (BP) neural network model on the bias of the once-time corrected Sims model is built. Ultimately, the double correction of the Sims model is realized through the additive compensation method, and an integrated model of rolling force is ultimately obtained. By comparing, it is shown that the average error of the traditional Sims model is as high as 34.22%. This error can be minimized to 9.40% with once-time correction and further reduced to 3.06% with the double correction. The results show that the prediction accuracy of the rolling force can be improved gradually by the proposed composite rectification method, and the bias brought by model influence factor and mathematical formcan be eliminated. The composite rectification method presented in this article can provide a new way of modeling complex systems with high precision. Industrial big data Generalized additive principle Neural network Rolling force modeling Full Text Additional Declarations No competing interests reported. Supplementary Files BP.xlsx FitnessFunction.m Untitled00.m Untitled11.m Untitled2.m Untitledt.m cluster1.m cluster2.m fun1.m gy1.m 1.xlsx 1.xlsx BP1.xlsx file.xlsx file.xlsx 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-3737740","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264122054,"identity":"183247f4-4fd9-4d0a-89d0-d2d2ee97f929","order_by":0,"name":"Xiao Xiao 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