{"paper_id":"1a96641e-2423-4eb2-8a1c-b67034b33782","body_text":"AI Driven Autonomous Adaptative Feedback Welding Machine | 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 AI Driven Autonomous Adaptative Feedback Welding Machine Benedikt von Querfurth, Shems-Eddine Belhout, Christian Knaak, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4999627/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 The Gas Tungsten Arc Welding (GTAW) is a primary method for nuclear component fabrication and repair. Recent advancements in monitoring and automation technologies have made the shift toward fully automated arc welding more feasible, reducing the necessity for continuous human oversight. Two artificial intelligence-based networks were developed that utilize sensor-based feedback on a mechanized GTAW head. We present an image-based semantic segmentation convolutional neural network that identifies crucial features such as the weld pool, groove, wire, and electrode based on which geometric measurements are derived. A separate novel neural network predicts the weld bead geometry for multi-pass welds and inconsistent groove geometries. The application of both neural networks is a pre-requisite that enables the autonomous planning and execution of multi-pass welds to fill a groove. Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 01 Nov, 2024 Reviewers invited by journal 31 Oct, 2024 Editor invited by journal 31 Oct, 2024 Editor assigned by journal 23 Oct, 2024 First submitted to journal 23 Oct, 2024 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-4999627\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":372726127,\"identity\":\"c9c6454e-6006-4bc1-95f4-1da36fab1d64\",\"order_by\":0,\"name\":\"Benedikt von Querfurth\",\"email\":\"\",\"orcid\":\"https://orcid.org/0009-0001-0384-7923\",\"institution\":\"FhG-ILT: Fraunhofer-Institut fur Lasertechnik ILT\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Benedikt\",\"middleName\":\"\",\"lastName\":\"von 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