Process monitoring and quality prediction system of GMAW robot welding based on digital twin

Process monitoring and quality prediction system of GMAW robot welding based on digital twin | 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 Process monitoring and quality prediction system of GMAW robot welding based on digital twin Xianhua Tan, Ke Liu, Yongcheng Lin, Mingsong Chen, Guanqiang Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6551537/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 In the process of gas metal arc welding (GMAW), it is essential to ensure the quality of the weld seam. Traditional quality control methods suffer from latency and insufficient intelligence. To address these limitations, a process monitoring and quality prediction system of GMAW robot welding based on digital twin is proposed. Firstly, a five-dimensional model framework based on digital twin is proposed, and the construction process of the system is elaborated in detail. Then, a data management process based on the data-driven method is designed, and a digital twin model of robot welding is constructed, which integrates sensor data to achieve dynamic simulation and real-time process monitoring of robot welding. Besides, a quality prediction model based on BP neural network optimized by genetic algorithm is established, and the quality is quantified in combination with an evaluation mechanism. Finally, the GMAW system, grounded in the digital twin concept and integrating the physical platform with virtual model, has been developed and experimentally validated. The experimental results demonstrate that the system is highly practical, providing an effective solution for the real-time monitoring and prediction of welding quality. GMAW Digital twin Process monitoring Quality prediction Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revisions Needed 17 Mar, 2026 Reviewers agreed at journal 14 May, 2025 Reviewers invited by journal 14 May, 2025 Editor assigned by journal 30 Apr, 2025 First submitted to journal 28 Apr, 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. 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-6551537","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456488913,"identity":"c23c03ab-f185-48ad-8838-bd708d785695","order_by":0,"name":"Xianhua Tan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACZjACsw5A6ANEapFgYGBLIFILA1wLjwFxWgyOMz97XNhmV8c/u+fjx59tDHJ8NxIYPxfg0SLZzGZuPLMtWULiztnN0rxtDMaSNxKYpWfg0cLPzGAGVMkswXAjdxszYxtD4oYbCWzMPHi0sDGzfwNqqZeQv5HzjBHosHqCWviZeUC2HJYwuJHDxgB0WIIBIS2SzTxl0jznjktuvJFmDGRIGM4887BZGp8Wg/PHt0nzlFXzy91IfvjxR5mNPN/x5IOf8WlBB8D4YWBsIEHDKBgFo2AUjAJsAADWY0Fy1TzKbQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6285-6774","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Xianhua","middleName":"","lastName":"Tan","suffix":""},{"id":456488914,"identity":"750b37cf-b709-4f6e-a5aa-baaf6a01fb73","order_by":1,"name":"Ke Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Liu","suffix":""},{"id":456488915,"identity":"329e8efb-b87c-40bd-850f-0566fd3ac567","order_by":2,"name":"Yongcheng Lin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yongcheng","middleName":"","lastName":"Lin","suffix":""},{"id":456488916,"identity":"cf5987a3-63d9-4dde-9ed8-16ce3fed8dc4","order_by":3,"name":"Mingsong Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mingsong","middleName":"","lastName":"Chen","suffix":""},{"id":456488917,"identity":"cdfd4d66-a036-484c-822d-d480ab568c78","order_by":4,"name":"Guanqiang Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Guanqiang","middleName":"","lastName":"Wang","suffix":""},{"id":456488918,"identity":"26e0892f-de76-41d9-a16e-af5bcf46d034","order_by":5,"name":"He Zeng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2025-04-29 01:57:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6551537/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6551537/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82914000,"identity":"b8985a05-bbea-44db-baca-e83e705ca68c","added_by":"auto","created_at":"2025-05-16 15:51:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1905468,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6551537/v1_covered_a7bbfba5-155c-4c2a-a7a1-db5c8c2b75c5.pdf"}],"financialInterests":"","formattedTitle":"Process monitoring and quality prediction system of GMAW robot welding based on digital twin","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":"GMAW, Digital twin, Process monitoring, Quality prediction","lastPublishedDoi":"10.21203/rs.3.rs-6551537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6551537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the process of gas metal arc welding (GMAW), it is essential to ensure the quality of the weld seam. Traditional quality control methods suffer from latency and insufficient intelligence. To address these limitations, a process monitoring and quality prediction system of GMAW robot welding based on digital twin is proposed. Firstly, a five-dimensional model framework based on digital twin is proposed, and the construction process of the system is elaborated in detail. Then, a data management process based on the data-driven method is designed, and a digital twin model of robot welding is constructed, which integrates sensor data to achieve dynamic simulation and real-time process monitoring of robot welding. Besides, a quality prediction model based on BP neural network optimized by genetic algorithm is established, and the quality is quantified in combination with an evaluation mechanism. Finally, the GMAW system, grounded in the digital twin concept and integrating the physical platform with virtual model, has been developed and experimentally validated. The experimental results demonstrate that the system is highly practical, providing an effective solution for the real-time monitoring and prediction of welding quality.\u003c/p\u003e","manuscriptTitle":"Process monitoring and quality prediction system of GMAW robot welding based on digital twin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 15:18:59","doi":"10.21203/rs.3.rs-6551537/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2026-03-18T03:36:50+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-05-14T11:17:08+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-14T11:06:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-01T03:54:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-04-28T21:57:09+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":"a3aa2f29-706c-460f-9279-ea2a63951a11","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T03:07:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 15:18:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6551537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6551537","identity":"rs-6551537","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}