Surrogate model and machine learning approaches for thermal field reconstruction from weld pool contour: application to GTA welding

Surrogate model and machine learning approaches for thermal field reconstruction from weld pool contour: application to GTA welding | 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 Surrogate model and machine learning approaches for thermal field reconstruction from weld pool contour: application to GTA welding Zaid Boutaleb, Issam Bendaoud, Sébastien Rouquette, Fabien Soulié This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5422383/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Thermal cycles in arc welding are crucial as they determine the metallurgy, residual stresses and distortions of welded parts. Experimentally measuring the temperature everywhere in the welded parts is not possible. This can be achieved with a thermal simulation but finite element analysis requires long computational times especially for large parts. This study aimed to predict the thermal field using a data-driven approach using numerical and experimental data. First, thermal modeling is defined and arc heating is described with an equivalent heat source. The numerical design of experiments was conducted by varying the heat source parameters. Weld pool contour is extracted from each simulations for building a numerical dataset. The numerical dataset is used for training a surrogate model. The surrogate model is used for estimating the heat source parameters from the weld pool contour using an optimization technique. Then a K-Nearest Neighbors algorithm is used to predict the thermal field from the estimated heat source parameters. A significant reduction in computational time is obtained for predicting the thermal field from experimental weld pool contour. Numerical analysis showed that the predicted thermal field is fairly good in the solid than in the weld pool. Weld Pool contour Heat flux modeling Surrogate Model linear regression Machine learning KNN method Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 22 Nov, 2024 Reviewers invited by journal 12 Nov, 2024 Editor invited by journal 12 Nov, 2024 Editor assigned by journal 12 Nov, 2024 First submitted to journal 11 Nov, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5422383","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377409875,"identity":"1f688fe1-c399-4e5a-ba1c-94a5a630bbea","order_by":0,"name":"Zaid Boutaleb","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0008-6173-3511","institution":"Universite de Montpellier","correspondingAuthor":true,"prefix":"","firstName":"Zaid","middleName":"","lastName":"Boutaleb","suffix":""},{"id":377409876,"identity":"d4c9053b-cb3c-455e-b056-bd7feceea858","order_by":1,"name":"Issam Bendaoud","email":"","orcid":"","institution":"Universite de Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Issam","middleName":"","lastName":"Bendaoud","suffix":""},{"id":377409877,"identity":"da39e107-c6fc-49c2-aefb-18993bde81c9","order_by":2,"name":"Sébastien Rouquette","email":"","orcid":"","institution":"Universite de Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Sébastien","middleName":"","lastName":"Rouquette","suffix":""},{"id":377409878,"identity":"fac21f88-1a4b-4e34-911a-f0d7f8c1aef4","order_by":3,"name":"Fabien Soulié","email":"","orcid":"","institution":"Universite de Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Fabien","middleName":"","lastName":"Soulié","suffix":""}],"badges":[],"createdAt":"2024-11-09 14:32:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5422383/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5422383/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70626590,"identity":"0061e0a6-f118-44fe-bcdd-41a8cf488cba","added_by":"auto","created_at":"2024-12-05 04:51:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3847665,"visible":true,"origin":"","legend":"","description":"","filename":"WITWD2400691.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5422383/v1_covered_dd7b8c3f-bc17-4141-96b2-f9df0382c681.pdf"}],"financialInterests":"","formattedTitle":"Surrogate model and machine learning approaches for thermal field reconstruction from weld pool contour: application to GTA welding","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":"welding-in-the-world","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"witw","sideBox":"Learn more about [Welding in the World](https://www.springer.com/journal/40194)","snPcode":"40194","submissionUrl":"https://www.editorialmanager.com/witw/","title":"Welding in the World","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Weld Pool contour, Heat flux modeling, Surrogate Model, linear regression, Machine learning, KNN method","lastPublishedDoi":"10.21203/rs.3.rs-5422383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5422383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThermal cycles in arc welding are crucial as they determine the metallurgy, residual stresses and distortions of welded parts. 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