Optimized Wear Behavior of LM13/ Zircon/Carbon Hybrid Composites: Experimental Analysis, Statistical Modeling, and Machine Learning Predictions

Optimized Wear Behavior of LM13/ Zircon/Carbon Hybrid Composites: Experimental Analysis, Statistical Modeling, and Machine Learning Predictions | 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 Optimized Wear Behavior of LM13/ Zircon/Carbon Hybrid Composites: Experimental Analysis, Statistical Modeling, and Machine Learning Predictions Ranjitha P, Premchand R, Rajanish M, Mahadeva Prasad, Ravitej Y P, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8429914/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This Paper investigates the wear behaviour of Aluminum-alloy (LM13) specimens cast under varying chill levels (12%, 9%, 6%, 3%, and as-cast) and quantitatively evaluates their tribological response through experimental, regression modelling and statistical analysis. Wear tests conducted at multiple rotational speeds showed a consistent increase in wear rate with RPM. Polynomial regression produced the highest predictive accuracy (R² = 0.9524–1.000), outperforming linear, Random Forest, and SVR models. Error metrics remained extremely low for all chill levels (RMSE = 0.000019–0.000047 g; MAE = 0.000014–0.000044 g), confirming the stability of the datasets. Shapiro–Wilk results (p > 0.314) and QQ-plots validated the normality of residuals, supporting the reliability of the statistical models. Microstructural analysis revealed that higher chill levels generated finer dendritic arm spacing, reduced porosity, and enhanced phase distribution uniformity. These features contributed to lower wear rates and reduced sensitivity to RPM, as reflected by minimal slope values (≈ 1.0×10⁻⁶ for 12% chill). In contrast, lower chill and as-cast samples exhibited coarser dendrites and weaker matrix integrity, resulting in higher wear and increased rate sensitivity. Overall, the study establishes that chill-induced microstructural refinement significantly enhances wear resistance and stabilizes the wear–speed relationship, providing a strong quantitative basis for optimizing casting parameters in tribological applications. Chill casting Wear rate Machine learning Tribological behavior Shapiro–Wilk normality Feature-importance analysis Chill casting Wear rate Microstructural refinement Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Feb, 2026 Reviews received at journal 31 Jan, 2026 Reviews received at journal 26 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor invited by journal 20 Jan, 2026 Editor assigned by journal 31 Dec, 2025 Submission checks completed at journal 31 Dec, 2025 First submitted to journal 22 Dec, 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-8429914","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578095456,"identity":"e5dc2abc-3fc7-4a02-9595-9b545ccccbbd","order_by":0,"name":"Ranjitha P","email":"","orcid":"","institution":"Dayananda sagar College of engineering","correspondingAuthor":false,"prefix":"","firstName":"Ranjitha","middleName":"","lastName":"P","suffix":""},{"id":578095457,"identity":"82785b9c-c903-4f1e-8d14-934019d07298","order_by":1,"name":"Premchand R","email":"","orcid":"","institution":"Bangalore Institute of Technology and 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Predictions","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":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chill casting, Wear rate, Machine learning, Tribological behavior, Shapiro–Wilk normality, Feature-importance analysis, Chill casting, Wear rate, Microstructural refinement","lastPublishedDoi":"10.21203/rs.3.rs-8429914/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8429914/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis Paper investigates the wear behaviour of Aluminum-alloy (LM13) specimens cast under varying chill levels (12%, 9%, 6%, 3%, and as-cast) and quantitatively evaluates their tribological response through experimental, regression modelling and statistical analysis. Wear tests conducted at multiple rotational speeds showed a consistent increase in wear rate with RPM. Polynomial regression produced the highest predictive accuracy (R\u0026sup2; = 0.9524\u0026ndash;1.000), outperforming linear, Random Forest, and SVR models. Error metrics remained extremely low for all chill levels (RMSE\u0026thinsp;=\u0026thinsp;0.000019\u0026ndash;0.000047 g; MAE\u0026thinsp;=\u0026thinsp;0.000014\u0026ndash;0.000044 g), confirming the stability of the datasets. Shapiro\u0026ndash;Wilk results (p\u0026thinsp;\u0026gt;\u0026thinsp;0.314) and QQ-plots validated the normality of residuals, supporting the reliability of the statistical models. Microstructural analysis revealed that higher chill levels generated finer dendritic arm spacing, reduced porosity, and enhanced phase distribution uniformity. These features contributed to lower wear rates and reduced sensitivity to RPM, as reflected by minimal slope values (\u0026asymp;\u0026thinsp;1.0\u0026times;10⁻⁶ for 12% chill). In contrast, lower chill and as-cast samples exhibited coarser dendrites and weaker matrix integrity, resulting in higher wear and increased rate sensitivity. Overall, the study establishes that chill-induced microstructural refinement significantly enhances wear resistance and stabilizes the wear\u0026ndash;speed relationship, providing a strong quantitative basis for optimizing casting parameters in tribological applications.\u003c/p\u003e","manuscriptTitle":"Optimized Wear Behavior of LM13/ Zircon/Carbon Hybrid Composites: Experimental Analysis, Statistical Modeling, and Machine Learning Predictions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 15:31:56","doi":"10.21203/rs.3.rs-8429914/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-02T19:30:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-31T11:26:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T14:54:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179444207354777903905329126286941181286","date":"2026-01-21T11:47:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260056373759814418938611746303646662195","date":"2026-01-21T11:07:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42229721073334896350218164308062598174","date":"2026-01-21T11:06:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T10:56:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-20T17:49:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-31T07:32:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-31T07:32:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2025-12-23T04:09:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2f69f164-f172-4c99-92b8-c7164c5b5e7d","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T10:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 15:31:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8429914","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8429914","identity":"rs-8429914","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}