Modeling, Validation and Multi-Objective Optimization of Al-Si/SiC-ZnO-Graphite Composites: Integrating Neural Networks-Genetic Algorithm with Interaction Effect Modeling for Tribological Performance

Modeling, Validation and Multi-Objective Optimization of Al-Si/SiC-ZnO-Graphite Composites: Integrating Neural Networks-Genetic Algorithm with Interaction Effect Modeling for Tribological Performance | 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 Modeling, Validation and Multi-Objective Optimization of Al-Si/SiC-ZnO-Graphite Composites: Integrating Neural Networks-Genetic Algorithm with Interaction Effect Modeling for Tribological Performance Jimmy Lolu Olajide, Isaac Thlabadira, Tarzaan Ezekiel Ayough This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7854165/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Mar, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract While main effects of processing parameters on aluminum matrix composite tribology have been extensively studied, interactive effects of reinforcement parameters—which can dominate performance—remain inadequately understood. This research systematically investigates reinforcement content, composition, and critically, their synergistic interaction, on wear behavior of hypereutectic Al-Si composites reinforced with zinc oxide, silicon carbide, and graphite fabricated from recycled piston scrap. Three predictive models were developed: Two-Factor Interaction from Design of Experiments, and Artificial Neural Networks employing Levenberg-Marquardt and Bayesian Regularization algorithms. Digital Image Processing quantified porosity and surface roughness from microstructures and worn surfaces. Hierarchical effects analysis revealed that reinforcement interaction exhibited dominant influence on wear performance (effect magnitude 156.15), exceeding individual effects of composition (95.0) and content (94.8), providing quantitative evidence that synergistic mechanisms rather than additive contributions govern tribological behavior. All models demonstrated exceptional correlation (R² ≥ 0.999), validated through ANOVA, Kruskal-Wallis, Levene's, Mood's Median, and Bonferroni tests. Desirability Function Analysis and Genetic Algorithm optimization converged to identical conditions (RCMP7, 11 wt.%), predicting minimum wear of 196.59 and 194.98 mg/min respectively, experimentally verified at 197 mg/min (0.2% deviation). The optimized composite achieved 97.48% wear reduction versus monolithic alloy and 61.3% weight reduction versus cast iron for cylinder liner applications, while maintaining fracture toughness (28.6 MPa·m^1/2) exceeding structural requirements. This work establishes interaction-focused optimization as essential for high-performance tribological composite development, with practical implications for automotive lightweighting and fuel efficiency improvement. Composites Tribology Hypereutectic Al-Si Alloy Design of Experiment Artificial Neural Network Genetic Algorithm Image Processing Full Text Cite Share Download PDF Status: Published Journal Publication published 02 Mar, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 06 Dec, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 17 Oct, 2025 Editor assigned by journal 17 Oct, 2025 First submitted to journal 16 Oct, 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. 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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-7854165","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531257812,"identity":"20db043e-c1a8-4fbd-b746-103c10604748","order_by":0,"name":"Jimmy Lolu 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Performance","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":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Composites Tribology, Hypereutectic Al-Si Alloy, Design of Experiment, Artificial Neural Network, Genetic Algorithm, Image Processing","lastPublishedDoi":"10.21203/rs.3.rs-7854165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7854165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhile main effects of processing parameters on aluminum matrix composite tribology have been extensively studied, interactive effects of reinforcement parameters—which can dominate performance—remain inadequately understood. This research systematically investigates reinforcement content, composition, and critically, their synergistic interaction, on wear behavior of hypereutectic Al-Si composites reinforced with zinc oxide, silicon carbide, and graphite fabricated from recycled piston scrap. Three predictive models were developed: Two-Factor Interaction from Design of Experiments, and Artificial Neural Networks employing Levenberg-Marquardt and Bayesian Regularization algorithms. Digital Image Processing quantified porosity and surface roughness from microstructures and worn surfaces. Hierarchical effects analysis revealed that reinforcement interaction exhibited dominant influence on wear performance (effect magnitude 156.15), exceeding individual effects of composition (95.0) and content (94.8), providing quantitative evidence that synergistic mechanisms rather than additive contributions govern tribological behavior. All models demonstrated exceptional correlation (R² ≥ 0.999), validated through ANOVA, Kruskal-Wallis, Levene's, Mood's Median, and Bonferroni tests. Desirability Function Analysis and Genetic Algorithm optimization converged to identical conditions (RCMP7, 11 wt.%), predicting minimum wear of 196.59 and 194.98 mg/min respectively, experimentally verified at 197 mg/min (0.2% deviation). The optimized composite achieved 97.48% wear reduction versus monolithic alloy and 61.3% weight reduction versus cast iron for cylinder liner applications, while maintaining fracture toughness (28.6 MPa·m^1/2) exceeding structural requirements. This work establishes interaction-focused optimization as essential for high-performance tribological composite development, with practical implications for automotive lightweighting and fuel efficiency improvement.\u003c/p\u003e","manuscriptTitle":"Modeling, Validation and Multi-Objective Optimization of Al-Si/SiC-ZnO-Graphite Composites: Integrating Neural Networks-Genetic Algorithm with Interaction Effect Modeling for Tribological Performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 05:44:59","doi":"10.21203/rs.3.rs-7854165/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2025-12-06T09:08:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-11-25T17:08:18+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-17T13:51:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-17T13:22:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-10-16T10:52:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9f1fc0d0-402c-4a8e-8bdf-4b2f0b6da820","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:05:27+00:00","versionOfRecord":{"articleIdentity":"rs-7854165","link":"https://doi.org/10.1007/s00170-026-17577-w","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2026-03-02 15:58:45","publishedOnDateReadable":"March 2nd, 2026"},"versionCreatedAt":"2025-10-30 05:44:59","video":"","vorDoi":"10.1007/s00170-026-17577-w","vorDoiUrl":"https://doi.org/10.1007/s00170-026-17577-w","workflowStages":[]},"version":"v1","identity":"rs-7854165","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7854165","identity":"rs-7854165","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}