Directional Surface Roughness Parameters as Predictors of Polymer Wear Debris Size under Dry and Lubricated Conditions: An Experimental and Machine Learning Approach

Directional Surface Roughness Parameters as Predictors of Polymer Wear Debris Size under Dry and Lubricated Conditions: An Experimental and Machine Learning Approach | 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 Directional Surface Roughness Parameters as Predictors of Polymer Wear Debris Size under Dry and Lubricated Conditions: An Experimental and Machine Learning Approach Rajeev Kumar Gupta, G S Divya, Rayappa Shrinivas Mahale, Reena Y A, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7629347/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract This study investigates the influence of counter surface directional roughness parameters on polymer wear debris size under dry and distilled water-lubricated conditions, while integrating machine learning to enhance predictive capability. High-density polyethylene pins were tested against stainless steel plates with varied grit-induced roughness and orientations: unidirectional parallel (UPL), unidirectional perpendicular (UPD), and criss-cross (CC). Debris size, quantified via equivalent circle diameter (ECD) using SEM and image analysis, was correlated with two novel 2D roughness parameter categories measured parallel (Ra‖) and perpendicular (Ra⊥) to the sliding direction. Under dry conditions, debris size showed the strongest correlation with Δa‖ (r = 0.94), whereas under lubrication, Δa⊥ (r = 0.93) was most predictive, highlighting the advantages of directional over conventional 3D parameters. Experimental datasets combining grit size, surface topography, and lubrication were used to train multiple regression models: Linear, Polynomial, Decision Tree, Random Forest, and Support Vector Regression (SVR). Polynomial Regression and Decision Tree consistently delivered the highest accuracy (R² ≈ 1.00), effectively capturing nonlinear dependencies and validating experimental observations of topography lubrication interactions. The combined experimental and machine learning approach provides a robust framework for predicting and controlling wear debris size, offering valuable insights for optimizing implant surface design to minimize biologically detrimental debris formation and extend prosthetic service life. Polymer wear debris Surface topography Equivalent circle diameter (ECD) Dry and lubricated conditions Machine learning prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 May, 2026 Reviews received at journal 14 Nov, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers invited by journal 27 Oct, 2025 Editor assigned by journal 17 Sep, 2025 Submission checks completed at journal 17 Sep, 2025 First submitted to journal 16 Sep, 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. 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-7629347","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537033834,"identity":"0805931b-7c89-45f2-a447-eaf9d85c48cf","order_by":0,"name":"Rajeev Kumar Gupta","email":"","orcid":"","institution":"Dayananda Sagar University","correspondingAuthor":false,"prefix":"","firstName":"Rajeev","middleName":"Kumar","lastName":"Gupta","suffix":""},{"id":537033835,"identity":"d1eb0bbb-d014-4e7e-b68a-830c42fe8358","order_by":1,"name":"G S Divya","email":"","orcid":"","institution":"Dayananda Sagar College of 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Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-bio--and-tribo-corrosion","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jbtc","sideBox":"Learn more about [Journal of Bio- and Tribo-Corrosion](http://link.springer.com/journal/40735)","snPcode":"40735","submissionUrl":"https://submission.nature.com/new-submission/40735/3","title":"Journal of Bio- and Tribo-Corrosion","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Polymer wear debris, Surface topography, Equivalent circle diameter (ECD), Dry and lubricated conditions, Machine learning prediction","lastPublishedDoi":"10.21203/rs.3.rs-7629347/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7629347/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the influence of counter surface directional roughness parameters on polymer wear debris size under dry and distilled water-lubricated conditions, while integrating machine learning to enhance predictive capability. High-density polyethylene pins were tested against stainless steel plates with varied grit-induced roughness and orientations: unidirectional parallel (UPL), unidirectional perpendicular (UPD), and criss-cross (CC). Debris size, quantified via equivalent circle diameter (ECD) using SEM and image analysis, was correlated with two novel 2D roughness parameter categories measured parallel (Ra‖) and perpendicular (Ra\u0026perp;) to the sliding direction. Under dry conditions, debris size showed the strongest correlation with Δa‖ (r\u0026thinsp;=\u0026thinsp;0.94), whereas under lubrication, Δa\u0026perp; (r\u0026thinsp;=\u0026thinsp;0.93) was most predictive, highlighting the advantages of directional over conventional 3D parameters. Experimental datasets combining grit size, surface topography, and lubrication were used to train multiple regression models: Linear, Polynomial, Decision Tree, Random Forest, and Support Vector Regression (SVR). Polynomial Regression and Decision Tree consistently delivered the highest accuracy (R\u0026sup2; \u0026asymp; 1.00), effectively capturing nonlinear dependencies and validating experimental observations of topography lubrication interactions. The combined experimental and machine learning approach provides a robust framework for predicting and controlling wear debris size, offering valuable insights for optimizing implant surface design to minimize biologically detrimental debris formation and extend prosthetic service life.\u003c/p\u003e","manuscriptTitle":"Directional Surface Roughness Parameters as Predictors of Polymer Wear Debris Size under Dry and Lubricated Conditions: An Experimental and Machine Learning Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 17:16:05","doi":"10.21203/rs.3.rs-7629347/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"263725264669025658716870562372125268664","date":"2026-05-07T10:14:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T18:39:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218391573867384274359969592617761512059","date":"2025-10-29T16:26:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T16:16:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T23:41:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-17T23:41:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Bio- and Tribo-Corrosion","date":"2025-09-16T10:32:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-bio--and-tribo-corrosion","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jbtc","sideBox":"Learn more about [Journal of Bio- and Tribo-Corrosion](http://link.springer.com/journal/40735)","snPcode":"40735","submissionUrl":"https://submission.nature.com/new-submission/40735/3","title":"Journal of Bio- and Tribo-Corrosion","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2f69f164-f172-4c99-92b8-c7164c5b5e7d","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-06T17:16:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 17:16:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7629347","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7629347","identity":"rs-7629347","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}