High-Speed Monitoring and Control of Forming Defects in Coarse-Wire MAG Welding Based on Multi-Frame Spatio-Temporal Neural Networks

High-Speed Monitoring and Control of Forming Defects in Coarse-Wire MAG Welding Based on Multi-Frame Spatio-Temporal Neural Networks | 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 High-Speed Monitoring and Control of Forming Defects in Coarse-Wire MAG Welding Based on Multi-Frame Spatio-Temporal Neural Networks Hao Yuan, Shibo Wei, JiaMin Yi, Chaoke Li, Ziran Wang, Zekun Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8235508/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 To mitigate undercut and humping defects induced by arc instability in high-speed coarse-wire MAG welding, this paper proposes a lightweight multi-frame spatio-temporal convolutional network for adaptive defect control. The network takes sequential multi-frame images as input, adopts a cross-loss function, and incorporates a reward–penalty mechanism to enhance defect recognition accuracy. Model light-weighting is realized through spatio-temporal decomposed convolutions, a depth-wise separable bottleneck, and global adaptive pooling, resulting in a 61% reduction in parameter count. Experiments conducted under different groove configurations and welding angles demonstrate that the overall performance is optimal when the input length is set to five frames. Across four welding states, the model achieves a macro-averaged F1 score of 0.8315 and an AUC of 0.965, with a recall of 0.82 for critical defects (undercut/humping). The recognition accuracy for all four welding states exceeds 90%, with an overall loss of 0.05 and an average response time of 1.5ms. Furthermore, the closed-loop adaptive control exhibits stable behavior, ensuring real-time performance, strong robustness, and industrial deploy-ability. This work provides practical guidance for the design of future adaptive welding systems targeting high-speed coarse-wire MAG processes. Convolutional Neural Networks High-Speed Coarse-Wire MAG Welding Multi-Frame Spatio-Temporal Fusion Forming Defect Monitoring PLC Closed-Loop Control Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 Dec, 2025 Reviewers invited by journal 17 Dec, 2025 Editor invited by journal 09 Dec, 2025 Editor assigned by journal 03 Dec, 2025 First submitted to journal 02 Dec, 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. 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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-8235508","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561635318,"identity":"d7c31629-674e-40fa-9ce9-95aa79e3637f","order_by":0,"name":"Hao 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Networks","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":"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":"Convolutional Neural Networks, High-Speed Coarse-Wire MAG Welding, Multi-Frame Spatio-Temporal Fusion, Forming Defect Monitoring, PLC Closed-Loop Control","lastPublishedDoi":"10.21203/rs.3.rs-8235508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8235508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo mitigate undercut and humping defects induced by arc instability in high-speed coarse-wire MAG welding, this paper proposes a lightweight multi-frame spatio-temporal convolutional network for adaptive defect control. The network takes sequential multi-frame images as input, adopts a cross-loss function, and incorporates a reward\u0026ndash;penalty mechanism to enhance defect recognition accuracy. Model light-weighting is realized through spatio-temporal decomposed convolutions, a depth-wise separable bottleneck, and global adaptive pooling, resulting in a 61% reduction in parameter count. Experiments conducted under different groove configurations and welding angles demonstrate that the overall performance is optimal when the input length is set to five frames. Across four welding states, the model achieves a macro-averaged F1 score of 0.8315 and an AUC of 0.965, with a recall of 0.82 for critical defects (undercut/humping). The recognition accuracy for all four welding states exceeds 90%, with an overall loss of 0.05 and an average response time of 1.5ms. Furthermore, the closed-loop adaptive control exhibits stable behavior, ensuring real-time performance, strong robustness, and industrial deploy-ability. This work provides practical guidance for the design of future adaptive welding systems targeting high-speed coarse-wire MAG processes.\u003c/p\u003e","manuscriptTitle":"High-Speed Monitoring and Control of Forming Defects in Coarse-Wire MAG Welding Based on Multi-Frame Spatio-Temporal Neural Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 17:15:57","doi":"10.21203/rs.3.rs-8235508/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-12-17T08:37:24+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T08:33:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Welding in the World","date":"2025-12-09T12:08:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-03T22:04:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Welding in the World","date":"2025-12-02T20:10:12+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"7b4430c4-7355-4a19-822a-3f26d5ba8fa7","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-25T15:36:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 17:15:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8235508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8235508","identity":"rs-8235508","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}