Tribological Performance of S-Glass and Graphene Reinforced Al6061 Composites: Experimental and Machine Learning Perspectives

Tribological Performance of S-Glass and Graphene Reinforced Al6061 Composites: Experimental and Machine Learning Perspectives | 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 Tribological Performance of S-Glass and Graphene Reinforced Al6061 Composites: Experimental and Machine Learning Perspectives Sripad Kulkarni, Rajanish M, Sandeep G M, Chethan K S, Vikas G, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9078843/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Hybrid metal matrix composites offer significant potential for improving the tribological performance of aluminium alloys used in high-speed engineering applications. In the present work, Al6061-based hybrid composites reinforced with varying contents of S-glass fibres (1–5 wt.%) and graphene (0.5–2 wt.%) were fabricated using a controlled stir casting technique. Dry sliding wear behaviour was systematically evaluated under different rotational speeds up to 500 RPM at a constant normal load. The results reveal that S-glass content plays a dominant role in reducing wear rate, with a pronounced minimum observed at approximately 3 wt.% S-glass, while graphene acts as a secondary reinforcement by promoting solid lubrication and tribofilm formation. Excessive reinforcement levels led to marginal performance deterioration due to dispersion-related effects. Microstructural and phase analyses confirmed stable matrix–reinforcement compatibility without the formation of detrimental secondary phases. To complement experimental observations, regression and machine learning models were employed to analyse non-linear trends, parameter sensitivity, and interaction effects. Quadratic regression provided the most physically consistent representation of wear behaviour, while residual diagnostics and cross-validation highlighted the limitations of complex models for small datasets. The combined experimental–computational approach enabled the identification of an optimal reinforcement window and provided deeper insight into wear mechanisms governing hybrid Al6061 composites. Al6061 composite S-glass fibre Graphene Wear behaviour Stir casting Regression analysis Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers invited by journal 07 May, 2026 Editor assigned by journal 10 Mar, 2026 Submission checks completed at journal 10 Mar, 2026 First submitted to journal 10 Mar, 2026 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-9078843","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":641515333,"identity":"2ce38efe-5cf0-4d6d-a8fe-99578cc2cbb6","order_by":0,"name":"Sripad Kulkarni","email":"","orcid":"","institution":"Dayananda Sagar University","correspondingAuthor":false,"prefix":"","firstName":"Sripad","middleName":"","lastName":"Kulkarni","suffix":""},{"id":641515334,"identity":"244caa51-d1ea-443b-adfb-d61ac6866611","order_by":1,"name":"Rajanish M","email":"","orcid":"","institution":"Dayananda Sagar Academy of Technology and 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In the present work, Al6061-based hybrid composites reinforced with varying contents of S-glass fibres (1\u0026ndash;5 wt.%) and graphene (0.5\u0026ndash;2 wt.%) were fabricated using a controlled stir casting technique. Dry sliding wear behaviour was systematically evaluated under different rotational speeds up to 500 RPM at a constant normal load. The results reveal that S-glass content plays a dominant role in reducing wear rate, with a pronounced minimum observed at approximately 3 wt.% S-glass, while graphene acts as a secondary reinforcement by promoting solid lubrication and tribofilm formation. Excessive reinforcement levels led to marginal performance deterioration due to dispersion-related effects. Microstructural and phase analyses confirmed stable matrix\u0026ndash;reinforcement compatibility without the formation of detrimental secondary phases. To complement experimental observations, regression and machine learning models were employed to analyse non-linear trends, parameter sensitivity, and interaction effects. Quadratic regression provided the most physically consistent representation of wear behaviour, while residual diagnostics and cross-validation highlighted the limitations of complex models for small datasets. 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