Triboinformatic modeling of wear and friction coefficient of microwave-assisted synthesized g-C3N4/MoS2 nanocomposites using advanced regression models

Triboinformatic modeling of wear and friction coefficient of microwave-assisted synthesized g-C3N4/MoS2 nanocomposites using advanced regression models | 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 Triboinformatic modeling of wear and friction coefficient of microwave-assisted synthesized g-C3N4/MoS2 nanocomposites using advanced regression models MUKUL SAXENA, Anuj Kumar Saharma, Monika Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4878520/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jun, 2025 Read the published version in Journal of the Brazilian Society of Mechanical Sciences and Engineering → Version 1 posted 4 You are reading this latest preprint version Abstract Tribological phenomena, encompassing friction, wear, and lubrication, significantly impact the performance and efficiency of mechanical systems across various industries. This research investigates the application of machine learning approaches to minimize wear depth and coefficient of friction in tribometer systems by modeling the effects of applied load, sliding speed, and coating material. Through comprehensive experimentation and analysis, the influence of these critical parameters on the tribological responses is quantified. Several machine learning algorithms, including linear regression, decision trees, random forests, support vector regression, k-nearest neighbors, and neural networks, are employed to capture the complex relationships between the input parameters and the responses. The neural network model achieved the best performance with a low mean squared error (MSE) of 0.0023 and high R-squared of 0.9977 for predicting wear depth, along with an MSE of 567.89 and R-squared of 0.9654 for coefficient of friction predictions. Random forests also exhibited strong performance with testing MSE of 0.0298 and R-squared of 0.9671 for wear depth. Feature importance analysis identifies coating material as the most influential factor for wear depth, while sliding speed is the dominant factor (F-ratio of 8.589) affecting the coefficient of friction. Statistical significance testing confirms a substantial difference between the means (t-statistic of -14.544, p-value of 5.41e-31). Multiple linear regression analysis reveals an optimized input parameter combination of 10 kN applied load, 0.5 m/s sliding speed, and 9 Wt% of gCN, minimizing both tribological responses with low MSEs of 0.0214 (wear depth) and 12.8745 (coefficient of friction) on the training set. The study provides valuable insights into tribological optimization, highlighting the potential of machine learning techniques for enhancing system performance, efficiency, and durability. The findings contribute to a deeper understanding of tribological processes and pave the way for future research in developing advanced coatings, lubricants, and condition monitoring systems for tribological applications, Nanocomposite graphitic carbon nitride molybdenum disulfide tribology Machine Learning Full Text Cite Share Download PDF Status: Published Journal Publication published 09 Jun, 2025 Read the published version in Journal of the Brazilian Society of Mechanical Sciences and Engineering → Version 1 posted Reviewers agreed at journal 10 Aug, 2024 Reviewers invited by journal 10 Aug, 2024 Editor assigned by journal 08 Aug, 2024 First submitted to journal 07 Aug, 2024 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. 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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-4878520","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338509435,"identity":"b45c7420-688c-48d3-8e32-361c00fec444","order_by":0,"name":"MUKUL SAXENA","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-0445-5128","institution":"Noida Institute of Engineering and Technology","correspondingAuthor":true,"prefix":"","firstName":"MUKUL","middleName":"","lastName":"SAXENA","suffix":""},{"id":338509436,"identity":"f1b28a11-f167-4d74-8bc9-627e635df01b","order_by":1,"name":"Anuj Kumar Saharma","email":"","orcid":"","institution":"Dr APJ Abdul Kalam University","correspondingAuthor":false,"prefix":"","firstName":"Anuj","middleName":"Kumar","lastName":"Saharma","suffix":""},{"id":338509437,"identity":"be570976-7d2b-4042-a318-a3d7ae4ed212","order_by":2,"name":"Monika Singh","email":"","orcid":"","institution":"Dr APJ Abdul Kalam University","correspondingAuthor":false,"prefix":"","firstName":"Monika","middleName":"","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2024-08-08 06:19:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4878520/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4878520/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40430-025-05681-z","type":"published","date":"2025-06-09T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84726520,"identity":"c92067e8-74c7-40b2-a164-b08d51dfdc17","added_by":"auto","created_at":"2025-06-16 16:06:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":815101,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4878520/v1_covered_6e66ebf5-a955-4190-a3dd-3ea71d2c3db5.pdf"}],"financialInterests":"","formattedTitle":"Triboinformatic modeling of wear and friction coefficient of microwave-assisted synthesized g-C3N4/MoS2 nanocomposites using advanced regression models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-the-brazilian-society-of-mechanical-sciences-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmse","sideBox":"Learn more about [Journal of the Brazilian Society of Mechanical Sciences and Engineering](http://link.springer.com/journal/40430)","snPcode":"40430","submissionUrl":"https://www.editorialmanager.com/bmse/default2.aspx","title":"Journal of the Brazilian Society of Mechanical Sciences and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Nanocomposite, graphitic carbon nitride, molybdenum disulfide tribology, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-4878520/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4878520/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Tribological phenomena, encompassing friction, wear, and lubrication, significantly impact the performance and efficiency of mechanical systems across various industries. This research investigates the application of machine learning approaches to minimize wear depth and coefficient of friction in tribometer systems by modeling the effects of applied load, sliding speed, and coating material. Through comprehensive experimentation and analysis, the influence of these critical parameters on the tribological responses is quantified. Several machine learning algorithms, including linear regression, decision trees, random forests, support vector regression, k-nearest neighbors, and neural networks, are employed to capture the complex relationships between the input parameters and the responses. The neural network model achieved the best performance with a low mean squared error (MSE) of 0.0023 and high R-squared of 0.9977 for predicting wear depth, along with an MSE of 567.89 and R-squared of 0.9654 for coefficient of friction predictions. Random forests also exhibited strong performance with testing MSE of 0.0298 and R-squared of 0.9671 for wear depth. Feature importance analysis identifies coating material as the most influential factor for wear depth, while sliding speed is the dominant factor (F-ratio of 8.589) affecting the coefficient of friction. Statistical significance testing confirms a substantial difference between the means (t-statistic of -14.544, p-value of 5.41e-31). Multiple linear regression analysis reveals an optimized input parameter combination of 10 kN applied load, 0.5 m/s sliding speed, and 9 Wt% of gCN, minimizing both tribological responses with low MSEs of 0.0214 (wear depth) and 12.8745 (coefficient of friction) on the training set. The study provides valuable insights into tribological optimization, highlighting the potential of machine learning techniques for enhancing system performance, efficiency, and durability. 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