Parameter Determination Methods for Goldak Heat Source Model in Arc Welding

Parameter Determination Methods for Goldak Heat Source Model in Arc 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 Parameter Determination Methods for Goldak Heat Source Model in Arc Welding lin nie, Yongtian Zhang, Jiahao Yuan, Yujie Li, Yuantao Gu, Longzhi Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6830106/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 The application of the double-ellipsoid heat source model proposed by Goldak is prevalent in numerical simulations of arc welding, where the selection of model parameters significantly impacts computational accuracy and efficiency. However, the absence of quantitative methods for parameter description often necessitates reliance on trial-and-error calculations or previously experience. In this work, BP neural network, GA-BP neural network and second-order polynomial regression model are employed to establish the relationship between the heat source model parameters and arc welding process parameters. The modeling process utilizes 49 datasets sourced from publicly available studies on heat source parameters, with dimensionless processing applied to the data. The results show that the three models have significant performance differences in parameter estimation. Notably, the GA-BP neural network model demonstrates excellent predictive ability, as evidenced by an RMSE value of 0.09. The approach presented in this work is capable of precisely and effectively determining Goldak heat source parameters based on any given welding process parameters, with significant potential to improve the accuracy and computational efficiency of numerical simulations in arc welding. Neural network Genetic algorithm Regression method Heat source parameters Arc welding 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. 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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-6830106","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482240033,"identity":"547df936-fe8d-4e39-9bbe-e756364a3888","order_by":0,"name":"lin nie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIie3OsQrCMBCA4SuFdgl0zaK+QqSDSsVnSRDsIs4OghGhq2sfw9HxJNCp2lXo1Dfo6iJGi7i1GQXzL7nAfSEANtsv5oKDHKZ6wuZqQkCThZ6UKWmeV59tAzLyXcTqVPSDXZFRWEdC+hdsJZO9x1HkZUhReRTyWEiy4q2EKcJQJKU4giZOooSkhHWQoNbkum3Iw4gQ0AQ5exNpRLzXx+bDFFU45lkcJmTZQQpVVfdkNgjSc3WrN1Hv4Oft5BtFAK5Pz3BfF0jzXZvNZvuvnonyR8iCSv7XAAAAAElFTkSuQmCC","orcid":"","institution":"East China JiaoTong University","correspondingAuthor":true,"prefix":"","firstName":"lin","middleName":"","lastName":"nie","suffix":""},{"id":482240034,"identity":"bbc2914a-205e-43a3-b449-fa701ec6a835","order_by":1,"name":"Yongtian Zhang","email":"","orcid":"","institution":"East China JiaoTong University","correspondingAuthor":false,"prefix":"","firstName":"Yongtian","middleName":"","lastName":"Zhang","suffix":""},{"id":482240035,"identity":"9890bcca-64e5-40fb-b1e6-faaa6ca527af","order_by":2,"name":"Jiahao Yuan","email":"","orcid":"","institution":"East China JiaoTong University","correspondingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Yuan","suffix":""},{"id":482240036,"identity":"d9f6df0f-7a8e-4a44-9862-b7fc825335ac","order_by":3,"name":"Yujie Li","email":"","orcid":"","institution":"East China JiaoTong University","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Li","suffix":""},{"id":482240037,"identity":"6a308079-a2d2-4311-9f76-6e64d42f0ce5","order_by":4,"name":"Yuantao Gu","email":"","orcid":"","institution":"East China JiaoTong University","correspondingAuthor":false,"prefix":"","firstName":"Yuantao","middleName":"","lastName":"Gu","suffix":""},{"id":482240038,"identity":"5410409a-d632-43d4-a589-9d0a5852453c","order_by":5,"name":"Longzhi Zhao","email":"","orcid":"","institution":"East China JiaoTong University","correspondingAuthor":false,"prefix":"","firstName":"Longzhi","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-06-05 14:25:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6830106/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6830106/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98774715,"identity":"c74b2def-02e4-4b2f-a7f1-621f188f456e","added_by":"auto","created_at":"2025-12-22 12:11:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":662970,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6830106/v1_covered_332e59f1-9c16-42de-bd67-0e2da64ae11c.pdf"}],"financialInterests":"","formattedTitle":"Parameter Determination Methods for Goldak Heat Source Model in Arc Welding","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":"Neural network, Genetic algorithm, Regression method, Heat source parameters, Arc welding","lastPublishedDoi":"10.21203/rs.3.rs-6830106/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6830106/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe application of the double-ellipsoid heat source model proposed by Goldak is prevalent in numerical simulations of arc welding, where the selection of model parameters significantly impacts computational accuracy and efficiency. 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