Research on Welding Behavior Characterization and Quality Control Methods Based on Multi-Sensor Information Fusion

Research on Welding Behavior Characterization and Quality Control Methods Based on Multi-Sensor Information Fusion | 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 Research on Welding Behavior Characterization and Quality Control Methods Based on Multi-Sensor Information Fusion Tianqi Wang, Hongtao Xi, Xiao Li, Junjie He, Xinqi Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9091647/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 address the challenges of real-time monitoring and control in manual welding and enable quantitative analysis of welder behavior, this study develops a manual welding information acquisition system based on multi-sensor fusion. An integrated multi-information sensing platform, encompassing welding torch posture, displacement velocity, acoustic sensors, and molten pool vision modules, is constructed. Microsecond-level synchronous acquisition and integration of multimodal data are realized via LabVIEW core and Audacity auxiliary. Two sets of experiments (automated welding sensor verification and manual welding defect detection) are designed to determine Q235 steel plate welding parameters and non-steady-state operation tasks. Welding behavior features are extracted from torch movement stability and speed uniformity; welding acoustic signals are analyzed via time-frequency domain methods for defect identification; and an improved DeeplabV3 + network is used for molten pool image segmentation to extract geometric dimensions. Experimental results verify the sensors’ high precision and reliability. Comparative analysis of defect-free and defective welding processes confirms that the synergistic stability of torch posture, welding speed, molten pool dynamics, and acoustic characteristics underpins high-quality weld formation, with transient anomalies in these signals serving as defect precursors. This work clarifies the causal relationship between multimodal process characteristics and manual welding quality. The proposed system and methods provide a reference for welder skill assessment and training, and lay a foundation for real-time multi-modal defect detection in robotic automated welding. Multi-sensor information fusion Manual welding information acquisition human welder behavior Weld pool contour Torch orientation and position Full Text Supplementary Files AuthorAgreement.doc Disclosureofpotentialconflictofinterest.doc Highlightsforreview.doc ImpactandScopeStatement.doc Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 Mar, 2026 Reviewers invited by journal 19 Mar, 2026 Editor invited by journal 18 Mar, 2026 Editor assigned by journal 11 Mar, 2026 First submitted to journal 10 Mar, 2026 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-9091647","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608921309,"identity":"a1fc2d62-c0f8-4cee-999a-3478599ce359","order_by":0,"name":"Tianqi 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