A Multi-task Interpretable Few-shot Learning Framework for Ultrasonic Welding Quality Recognition

A Multi-task Interpretable Few-shot Learning Framework for Ultrasonic Welding Quality Recognition | 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 Multi-task Interpretable Few-shot Learning Framework for Ultrasonic Welding Quality Recognition Jiahong chen, Qingyi Xiong, Zhen Yao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6981849/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 Ultrasonic metal welding (UMW) is a high-efficiency, low-thermal-impact solid-state bonding technique that has been widely employed in battery manufacturing, the automotive industry, and related fields. However, its performance is highly sensitive to process anomalies and variations introduced by new process configurations, which may result in significant degradation of weld quality and substantial production losses. Consequently, developing online monitoring approaches with strong generalization capability has become essential.Conventional methods often depend on large volumes of labeled data and lack adaptability to unseen process conditions. To address these limitations, this study proposes a novel multi-task interpretable few-shot learning framework, termed MXFSL. The framework integrates DE-VMD-based feature extraction, multi-task collaborative meta-learning, and a multi-scale SHAP interpretation module to enable efficient anomaly detection and feature attribution under limited data scenarios.Experimental results demonstrate that MXFSL achieves superior classification performance and robustness in few-shot settings. Furthermore, it identifies critical features contributing to welding quality across different stages, offering valuable insights for intelligent monitoring and adaptive process optimization in UMW applications. Ultrasonic Metal Welding Few-Shot Learning Multi-Task Learning Meta-Learning Online Monitoring Interpretability 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. 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-6981849","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491856825,"identity":"2624871e-a25f-48bf-abdc-c3722cbd1751","order_by":0,"name":"Jiahong chen","email":"","orcid":"","institution":"Guangdong University of Technology - University Town Campus: Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiahong","middleName":"","lastName":"chen","suffix":""},{"id":491856826,"identity":"7769bb3e-818b-4bb6-bafd-2ea0cbc0767a","order_by":1,"name":"Qingyi Xiong","email":"","orcid":"","institution":"Guangdong University of Technology - University Town Campus: Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Qingyi","middleName":"","lastName":"Xiong","suffix":""},{"id":491856827,"identity":"d2b74c8d-f8c6-4435-a9ef-a42f2addae36","order_by":2,"name":"Zhen Yao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYJACZiCWY2BIgLGJ1GIM1MLYQJKWxAaitRgcP3z4c0HFnfT57jnmDxgqrBMb2M8ewK/lTFqC8Ywzz3I3nnlj2MBwJj2xgScvAb+WAzkGybxth3M3zsgxbGBsO5zYIMFjgF/L+TcGh3n/HU43BGv5R4yWGzmGzbwNhxPkJUBaGojQInnjWTIzz7HDhht4nhXOSDiWbtzGk4NfC9/55MOfeWoOy8u3J2/48KHGWraf/Qx+LQoHYC4EMRKAmA2veiCQb0BnjIJRMApGwShABwBsQEri6BTIhQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-4251-712X","institution":"Guangdong University of Technology - University Town Campus: Guangdong University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Yao","suffix":""}],"badges":[],"createdAt":"2025-06-26 09:27:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6981849/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6981849/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90386442,"identity":"00f74fa1-12b6-4f83-8118-30ac26138bde","added_by":"auto","created_at":"2025-09-02 07:38:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3225073,"visible":true,"origin":"","legend":"","description":"","filename":"WITWD2500488reviewer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6981849/v1_covered_62e8811a-9eba-4be2-9c4a-c89db833345e.pdf"}],"financialInterests":"","formattedTitle":"A Multi-task Interpretable Few-shot Learning Framework for Ultrasonic Welding Quality Recognition","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":"Ultrasonic Metal Welding, Few-Shot Learning, Multi-Task Learning, Meta-Learning, Online Monitoring, Interpretability","lastPublishedDoi":"10.21203/rs.3.rs-6981849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6981849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e Ultrasonic metal welding (UMW) is a high-efficiency, low-thermal-impact solid-state bonding technique that has been widely employed in battery manufacturing, the automotive industry, and related fields. 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