A cross-condition benchmark and interpretability analysis for vision-based nugget size prediction in resistance spot welding

A cross-condition benchmark and interpretability analysis for vision-based nugget size prediction in resistance spot 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 A cross-condition benchmark and interpretability analysis for vision-based nugget size prediction in resistance spot welding WenJie WANG, Qiang SONG, YangTian LIN, WenLong XU, ChengQing ZHANG, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8194392/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 Resistance spot welding (RSW) demands reliable quality assessment, yet traditional inspection methods are often destructive, costly, or inefficient. While computer vision has been applied to RSW, existing studies are predominantly limited to the classification of surface defects. This study represents a pioneering attempt to achieve low-cost, non-destructive quality inspection by performing end-to-end regression prediction of internal nugget size directly from post-weld surface images. We constructed a comprehensive dataset spanning multiple sheet stack-ups and four typical fit-up conditions—standard, initial gap, electrode angle, and edge proximity. Five mainstream Convolutional Neural Network (CNN) architectures were trained and systematically evaluated to assess the potential of surface-to-internal quality mapping. The results highlight two core findings. First, regarding feasibility, the study demonstrates that advanced vision models combined with complete surface imagery can achieve practical prediction accuracy (R² >0.8) on in-distribution data, confirming that weld surface morphology contains physical cues highly predictive of nugget growth. Second, the study explicitly reveals and quantitatively analyzes a critical challenge: model generalization. All models exhibited severe performance degradation when the test distribution deviated from the training conditions, specifically under abnormal fit-up conditions and unseen material stack-ups. Grad-CAM interpretability analysis corroborates these trends, showing that while effective predictions rely on indentation borders and oxidation patterns, models struggling with generalization over-attend to background artifacts. This work clarifies both the practical applicability and the generalization boundaries of surface-vision-based regression, suggesting that future work must focus on domain adaptation techniques and data augmentation strategies to overcome distribution shifts in diverse industrial environments. Resistance spot welding Surface recognition Convolutional neural network Generalization ability Model interpretation 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-8194392","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551582660,"identity":"f8b9752a-b68f-45a5-8f06-efdfbc678a93","order_by":0,"name":"WenJie WANG","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"WenJie","middleName":"","lastName":"WANG","suffix":""},{"id":551582661,"identity":"0a3ebc47-6e32-4f88-ac2b-340ed073cc48","order_by":1,"name":"Qiang SONG","email":"","orcid":"","institution":"Shanghai Jiao Tong 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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":"Resistance spot welding, Surface recognition, Convolutional neural network, Generalization ability, Model interpretation","lastPublishedDoi":"10.21203/rs.3.rs-8194392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8194392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eResistance spot welding (RSW) demands reliable quality assessment, yet traditional inspection methods are often destructive, costly, or inefficient. While computer vision has been applied to RSW, existing studies are predominantly limited to the classification of surface defects. This study represents a pioneering attempt to achieve low-cost, non-destructive quality inspection by performing end-to-end regression prediction of internal nugget size directly from post-weld surface images. We constructed a comprehensive dataset spanning multiple sheet stack-ups and four typical fit-up conditions\u0026mdash;standard, initial gap, electrode angle, and edge proximity. Five mainstream Convolutional Neural Network (CNN) architectures were trained and systematically evaluated to assess the potential of surface-to-internal quality mapping. The results highlight two core findings. First, regarding feasibility, the study demonstrates that advanced vision models combined with complete surface imagery can achieve practical prediction accuracy (R\u0026sup2; \u0026gt;0.8) on in-distribution data, confirming that weld surface morphology contains physical cues highly predictive of nugget growth. Second, the study explicitly reveals and quantitatively analyzes a critical challenge: model generalization. All models exhibited severe performance degradation when the test distribution deviated from the training conditions, specifically under abnormal fit-up conditions and unseen material stack-ups. Grad-CAM interpretability analysis corroborates these trends, showing that while effective predictions rely on indentation borders and oxidation patterns, models struggling with generalization over-attend to background artifacts. This work clarifies both the practical applicability and the generalization boundaries of surface-vision-based regression, suggesting that future work must focus on domain adaptation techniques and data augmentation strategies to overcome distribution shifts in diverse industrial environments.\u003c/p\u003e","manuscriptTitle":"A cross-condition benchmark and interpretability analysis for vision-based nugget size prediction in resistance spot welding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 05:06:27","doi":"10.21203/rs.3.rs-8194392/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"e2b9fe91-8041-4629-b625-c66c530d5087","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-05T05:56:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 05:06:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8194392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8194392","identity":"rs-8194392","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}