Machine Learning Approaches for Predicting Stribeck Curves in Lubricated Contacts

Machine Learning Approaches for Predicting Stribeck Curves in Lubricated Contacts | 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 Machine Learning Approaches for Predicting Stribeck Curves in Lubricated Contacts Pedro Romio This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9135155/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 Machine elements frequently operate under variable conditions, resulting in significant variations in interfacial friction across different lubrication regimes. The Stribeck curve is a well-established tool for visualizing frictional behavior under boundary, mixed, and full-film lubrication conditions. While numerical models such as Thermo Plasto-Elastohydrodynamic Lubrication (TPEHL) provide accurate friction predictions, they are computationally demanding. This study investigates the application of Artificial Intelligence (AI) to predict the coefficient of friction in Stribeck curves, utilizing a comprehensive experimental dataset based on polyalphaolefin (PAO) oil RENOLIN UNISYN XT ISO VG 68. Three AI models - Neural Networks, Random Forest, and Support Vector Machine - were evaluated using cross-validation. Statistical analysis via Tukey’s Honestly Significant Difference (HSD) test demonstrated that the Random Forest model achieved superior predictive accuracy compared to the Neural Networks and Support Vector Machine models. Subsequently, the Random Forest model was applied to predict Stribeck curves for PAO RENOLIN UNISYN XT ISO VG 150, a lubricant of similar composition but higher viscosity, confirming its robustness and generalization capability across different lubricants. Neural Networks Random Forest Support Vector Machine Stribeck Curves 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. 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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-9135155","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625401193,"identity":"e075d1d3-6e9f-4ec3-8ce0-6e80b392d001","order_by":0,"name":"Pedro Romio","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYHACxgcJIIqZBC3MBkhaDIjSwiaBxCFCi3l7d1rFgz82eQzsvMceMO74Q1iLzJmz224ktqUVMzDzpRswniHCFgmJXKCWhsOJDcw8ZhKMbURqKUj4859ELQwJbAdI0cJzdrNEYltyMRvIL4lnjInQwt678eOPP3Z5/Pxnjz34uEOOsBYYSGBj4GFjSGwgXgdDAgNICyMpWkbBKBgFo2DEAABxvDJ7r0Pc0QAAAABJRU5ErkJggg==","orcid":"","institution":"Polytechnic Institute of Porto","correspondingAuthor":true,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Romio","suffix":""}],"badges":[],"createdAt":"2026-03-16 08:39:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9135155/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9135155/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107705976,"identity":"86362e9e-563f-48a2-bf0a-e297ea85da4b","added_by":"auto","created_at":"2026-04-24 09:16:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2688775,"visible":true,"origin":"","legend":"","description":"","filename":"MachineLearningApproachesforPredictingStribeckCurvesinLubricatedContactsArticleV2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9135155/v1_covered_7fe67ac7-ac04-412e-8a80-4bf351ed4932.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning Approaches for Predicting Stribeck Curves in Lubricated Contacts","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neural Networks, Random Forest, Support Vector Machine, Stribeck Curves","lastPublishedDoi":"10.21203/rs.3.rs-9135155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9135155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMachine elements frequently operate under variable conditions, resulting in significant variations in interfacial friction across different lubrication regimes. The Stribeck curve is a well-established tool for visualizing frictional behavior under boundary, mixed, and full-film lubrication conditions. While numerical models such as Thermo Plasto-Elastohydrodynamic Lubrication (TPEHL) provide accurate friction predictions, they are computationally demanding. This study investigates the application of Artificial Intelligence (AI) to predict the coefficient of friction in Stribeck curves, utilizing a comprehensive experimental dataset based on polyalphaolefin (PAO) oil RENOLIN UNISYN XT ISO VG 68. Three AI models - Neural Networks, Random Forest, and Support Vector Machine - were evaluated using cross-validation. Statistical analysis via Tukey\u0026rsquo;s Honestly Significant Difference (HSD) test demonstrated that the Random Forest model achieved superior predictive accuracy compared to the Neural Networks and Support Vector Machine models. Subsequently, the Random Forest model was applied to predict Stribeck curves for PAO RENOLIN UNISYN XT ISO VG 150, a lubricant of similar composition but higher viscosity, confirming its robustness and generalization capability across different lubricants.\u003c/p\u003e","manuscriptTitle":"Machine Learning Approaches for Predicting Stribeck Curves in Lubricated Contacts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 08:45:08","doi":"10.21203/rs.3.rs-9135155/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"704256d9-3a3c-4733-bf51-d3e04e439b73","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T08:45:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 08:45:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9135155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9135155","identity":"rs-9135155","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}