Digital Twin-Enabled Predictive Analytics for Friction Stir Welding: Multi-Physics Simulation, Sensor Synchronization, and Defect Detection

Digital Twin-Enabled Predictive Analytics for Friction Stir Welding: Multi-Physics Simulation, Sensor Synchronization, and Defect Detection | 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 Digital Twin-Enabled Predictive Analytics for Friction Stir Welding: Multi-Physics Simulation, Sensor Synchronization, and Defect Detection NABHAN TAWJIH YOUSEF, Rammah Yousef This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8702698/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Friction stir welding (FSW) is a solid-state joining process capable of producing high-quality, high-strength joints without material melting. However, the strong sensitivity of FSW to process parameters, material variations, and operational disturbances often leads to inconsistent weld quality, creating challenges for reliable industrial quality assurance. Although finite element simulations and experimental optimization studies have improved understanding of underlying thermo-mechanical behavior, most existing approaches remain offline and lack the ability to adapt to real-time process variations. This study presents a comprehensive digital twin framework for FSW by integrating multi-physics finite element simulations, real-time multi-sensor data acquisition, and machine-learning-based predictive analytics. The finite element model captures key physical phenomena, including heat transfer, material flow, and residual stress evolution during welding of AA6061-T6 aluminum alloy, providing a robust physics-based baseline. Real-time synchronization is achieved through thermal, force, and vibration sensor data, enabling continuous updating of the virtual model to reflect process disturbances such as tool wear and clamping variability. Machine learning models are embedded within the digital twin to enhance predictive capability. An artificial neural network predicts mechanical properties with a coefficient of determination (R²) exceeding 0.9, while a support vector machine classifier detects weld defects with accuracy above 92%. Experimental validation confirms that predicted tensile strength and hardness deviate by less than ±4% from measured values. The proposed framework enables real-time monitoring, predictive quality assurance, and proactive defect prevention, advancing friction stir welding toward intelligent, self-adaptive manufacturing aligned with Industry 4.0. Friction stir welding Digital twin Aluminum alloys Smart manufacturing Process optimization Industry 4.0 Full Text Cite Share Download PDF Status: Posted Version 1 posted 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-8702698","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586814454,"identity":"0bc9813f-a3c7-4370-8099-7b1854788f6c","order_by":0,"name":"NABHAN TAWJIH YOUSEF","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDACCR4wVd/A3gCkDCyI18LYwHMApEWCFC0SCWAuYR3m0r0HPxcwHGY2uPn86oYfBRIM/O3dCXi1WM45lyw9g+Ewm8HtnLKbPUCHSZw5uwGvFoMbOQbSPAyHeYBa0m7wALUYSOQS1GL8G6hFwuDmmbSbf4jUYgayxcDgBvux28TZcueMmTWPQXqC5JkcttsyBhI8hP1yu8f4Nk+FdQLf8ePPbr75YyPH396LXwtUYzODwgEeAxCThwjlYFDHIN/A/oBY1aNgFIyCUTDCAABehUeazxFjggAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1254-609X","institution":"Marwadi University","correspondingAuthor":true,"prefix":"","firstName":"NABHAN","middleName":"TAWJIH","lastName":"YOUSEF","suffix":""},{"id":586814455,"identity":"1095c1c2-8059-407f-8723-76627d6ba874","order_by":1,"name":"Rammah Yousef","email":"","orcid":"","institution":"Shoolini University","correspondingAuthor":false,"prefix":"","firstName":"Rammah","middleName":"","lastName":"Yousef","suffix":""}],"badges":[],"createdAt":"2026-01-26 17:16:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8702698/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8702698/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102748464,"identity":"75b246c9-0759-4d99-b084-ed9403613d8e","added_by":"auto","created_at":"2026-02-16 09:11:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":953225,"visible":true,"origin":"","legend":"","description":"","filename":"4DIGI1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8702698/v1_covered_21c38b11-a831-4ad1-95b4-4832e8c7adf8.pdf"}],"financialInterests":"","formattedTitle":"Digital Twin-Enabled Predictive Analytics for Friction Stir Welding: Multi-Physics Simulation, Sensor Synchronization, and Defect Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Friction stir welding, Digital twin, Aluminum alloys, Smart manufacturing, Process optimization, Industry 4.0","lastPublishedDoi":"10.21203/rs.3.rs-8702698/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8702698/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFriction stir welding (FSW) is a solid-state joining process capable of producing high-quality, high-strength joints without material melting. However, the strong sensitivity of FSW to process parameters, material variations, and operational disturbances often leads to inconsistent weld quality, creating challenges for reliable industrial quality assurance. Although finite element simulations and experimental optimization studies have improved understanding of underlying thermo-mechanical behavior, most existing approaches remain offline and lack the ability to adapt to real-time process variations.\u003c/p\u003e\n\u003cp\u003eThis study presents a comprehensive digital twin framework for FSW by integrating multi-physics finite element simulations, real-time multi-sensor data acquisition, and machine-learning-based predictive analytics. The finite element model captures key physical phenomena, including heat transfer, material flow, and residual stress evolution during welding of AA6061-T6 aluminum alloy, providing a robust physics-based baseline. 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