Arc length–voltage behaviour in GMAW integrating a voltage model and deep learning

Arc length–voltage behaviour in GMAW integrating a voltage model and deep learning | 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 Arc length–voltage behaviour in GMAW integrating a voltage model and deep learning J. Eduardo Alvarez-Rocha, Lucas Taipe, Patricio F. Mendez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8233258/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 This paper investigates the relationship between arc length and voltage in aluminum GMAW using a combination of deep-learning-based high-speed video analysis and a physics-based voltage model. A deep learning segmentation model was applied to approximately 15,000 high-speed video frames per video synchronized with corresponding electrical signals to extract arc length metrics. The method enabled quantitative estimation of the cathode and anode fall voltages (19.77 ± 0.61 V) and the arc column electric field (0.41 ± 0.02 V mm^-1). The arc attachment definition employed to obtain the arc length values for the voltage model was the top of the molten consumable. Additional characteristic arc lengths were analyzed during droplet formation and detachment cycles, revealing a sharp voltage drop after detachment, concurrent with an increase in arc length and reduced metal vapour presence in the arc column. A critical molten consumable length equal to the wire diameter was identified as a threshold to establish a metal vapour column. Overall, the approach demonstrates value in the use of deep learning to validate numerical and analytical models of the arc region in GMAW. Deep learning computer vision GMAW arc length fall voltage metal vapours Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 Feb, 2026 Reviewers invited by journal 06 Feb, 2026 Editor invited by journal 26 Jan, 2026 Editor assigned by journal 21 Jan, 2026 First submitted to journal 18 Jan, 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. 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-8233258","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586780556,"identity":"ea6b15d1-abb7-4c7f-ae68-493ac9f838d0","order_by":0,"name":"J. 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