Intelligent Hybrid-Layer Friction Drill Joining Using a Coupled Finite Element and Deep Learning Framework for Cast Al380 Enhancement

Intelligent Hybrid-Layer Friction Drill Joining Using a Coupled Finite Element and Deep Learning Framework for Cast Al380 Enhancement | 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 Article Intelligent Hybrid-Layer Friction Drill Joining Using a Coupled Finite Element and Deep Learning Framework for Cast Al380 Enhancement Sara El-Bahloul This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8603110/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Joining cast aluminum alloys remains a significant challenge due to their inherent brittleness and their propensity to crack under thermo-mechanical loading. This limitation is particularly evident in Al380, which exhibits severe petal formation and unstable bush development during friction drilling. To overcome these challenges, this study investigates the friction drill joining (FDJ) of Al380 using three distinct upper-sheet materials – Cu, Al6061, and AISI304 – with the aim of improving heat distribution, reducing crack initiation, and enhancing bush formation. A combined experimental, numerical, and data-driven methodology was adopted. Experimentally, friction drilling test was conducted under certain process conditions to validate the influence of upper-layer material on heat generation, material flow, and crack initiation. Complementary thermo-mechanical finite element simulations were developed using ABAQUS to evaluate temperature evolution, von Mises stress, and bushing formation behavior within the joint region, thereby elucidating the mechanisms governing improved formability in hybrid-layer assemblies. Experimental and numerical analyses revealed that the presence of an upper sheet significantly modified the heat generation and stress distribution within the joint zone, reducing crack initiation and improving material flow. A feed-forward artificial neural network (ANN) composed of multiple layers developed using the MATLAB Deep Learning Toolbox was further employed to predict bushing length, peak temperature, and stress response across different process conditions. The results demonstrate that integrating AL380 with dissimilar metallic upper layers effectively overcomes its brittleness and expands the applicability of friction drill joining for cast alloys, offering a promising route toward reliable lightweight multi-material assemblies. Physical sciences/Engineering Physical sciences/Materials science Friction Drill Joining Al380 Cast Aluminum Alloy Dissimilar Material Joining Thermo-Mechanical Modeling Crack Suppression Joint Strength Enhancement Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Editor assigned by journal 29 Jan, 2026 Editor invited by journal 28 Jan, 2026 Submission checks completed at journal 27 Jan, 2026 First submitted to journal 27 Jan, 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. 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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-8603110","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":583576771,"identity":"f0ed3b38-38bc-49eb-a3d9-7c44f08b1141","order_by":0,"name":"Sara El-Bahloul","email":"data:image/png;base64,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","orcid":"","institution":"Mansoura University","correspondingAuthor":true,"prefix":"","firstName":"Sara","middleName":"","lastName":"El-Bahloul","suffix":""}],"badges":[],"createdAt":"2026-01-14 15:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8603110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8603110/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101752714,"identity":"ff998a1c-d13e-4296-b7de-fdae0aba8559","added_by":"auto","created_at":"2026-02-03 10:29:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1664906,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8603110/v1_covered_08f48d99-1541-43d9-bd6a-ed99a011e95b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intelligent Hybrid-Layer Friction Drill Joining Using a Coupled Finite Element and Deep Learning Framework for Cast Al380 Enhancement","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Friction Drill Joining, Al380 Cast Aluminum Alloy, Dissimilar Material Joining, Thermo-Mechanical Modeling, Crack Suppression, Joint Strength Enhancement","lastPublishedDoi":"10.21203/rs.3.rs-8603110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8603110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eJoining cast aluminum alloys remains a significant challenge due to their inherent brittleness and their propensity to crack under thermo-mechanical loading. This limitation is particularly evident in Al380, which exhibits severe petal formation and unstable bush development during friction drilling. To overcome these challenges, this study investigates the friction drill joining (FDJ) of Al380 using three distinct upper-sheet materials \u0026ndash; Cu, Al6061, and AISI304 \u0026ndash; with the aim of improving heat distribution, reducing crack initiation, and enhancing bush formation. A combined experimental, numerical, and data-driven methodology was adopted. Experimentally, friction drilling test was conducted under certain process conditions to validate the influence of upper-layer material on heat generation, material flow, and crack initiation. Complementary thermo-mechanical finite element simulations were developed using ABAQUS to evaluate temperature evolution, von Mises stress, and bushing formation behavior within the joint region, thereby elucidating the mechanisms governing improved formability in hybrid-layer assemblies. Experimental and numerical analyses revealed that the presence of an upper sheet significantly modified the heat generation and stress distribution within the joint zone, reducing crack initiation and improving material flow. A feed-forward artificial neural network (ANN) composed of multiple layers developed using the MATLAB Deep Learning Toolbox was further employed to predict bushing length, peak temperature, and stress response across different process conditions. The results demonstrate that integrating AL380 with dissimilar metallic upper layers effectively overcomes its brittleness and expands the applicability of friction drill joining for cast alloys, offering a promising route toward reliable lightweight multi-material assemblies.\u003c/p\u003e","manuscriptTitle":"Intelligent Hybrid-Layer Friction Drill Joining Using a Coupled Finite Element and Deep Learning Framework for Cast Al380 Enhancement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 09:22:25","doi":"10.21203/rs.3.rs-8603110/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-19T11:54:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T06:33:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T22:52:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-07T01:05:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T06:38:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T21:42:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330899896907390643796450574391146763708","date":"2026-01-30T05:10:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250244897872757534770598004335216193506","date":"2026-01-29T16:10:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81805496759206872053297915214447638344","date":"2026-01-29T15:39:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210055549846990464564301339287591195457","date":"2026-01-29T15:39:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179848418843947338085595398247515701919","date":"2026-01-29T15:32:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T15:24:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T15:22:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-28T14:57:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-27T12:59:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-27T12:37:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d092eba-172d-434a-9671-8667aa393087","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62090977,"name":"Physical sciences/Engineering"},{"id":62090978,"name":"Physical sciences/Materials science"}],"tags":[],"updatedAt":"2026-03-27T14:38:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 09:22:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8603110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8603110","identity":"rs-8603110","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}