Investigation on Welding Sequence Optimization for Ship Structural Members Using Reinforcement Learning

Investigation on Welding Sequence Optimization for Ship Structural Members Using Reinforcement 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 Investigation on Welding Sequence Optimization for Ship Structural Members Using Reinforcement Learning Wenda Wang, Shintaro Maeda, Kazuki Ikushima, Kensaku Nishihara, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8643375/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Welding sequence strongly affects assembly distortion of thin-plate ship structures, but sequence planning still relies on skilled workers' experience and trial-and-error. This study proposes an AI welding-sequence optimization system integrating reinforcement learning with finite-element method (FEM) distortion analysis. The problem is formulated as a sequential decision process where states encode execution history and actions select the next weld line to minimize final deformation. For a four-weld-line specimen, Q-learning is applied and the derived sequences are validated by thermo-elasto-plastic FEM analysis and stereo-vision measurements of out-of-plane displacement and end-angle, confirming good agreement between analysis and experiments. For a ship-block-scale model with 22 weld lines, a Deep Q-Network (DQN) combined with inherent-strain-based FEM enables efficient search and reduces peak out-of-plane displacement from 30 mm to 15 mm. This study demonstrates that reinforcement learning can autonomously extract physically interpretable welding sequence strategies that control history-dependent thermal–mechanical interactions in multi-pass joining processes. The framework provides practical decision support for welding sequence planning in shipbuilding within realistic computation time. welding sequence welding distortion reinforcement learning Deep Q-Network finite element analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviews received at journal 07 Feb, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers invited by journal 22 Jan, 2026 Editor assigned by journal 22 Jan, 2026 Submission checks completed at journal 22 Jan, 2026 First submitted to journal 19 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. 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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-8643375","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580295816,"identity":"4680c32e-b0c4-46a3-b5d3-e92349f3263f","order_by":0,"name":"Wenda Wang","email":"","orcid":"","institution":"Osaka Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Wenda","middleName":"","lastName":"Wang","suffix":""},{"id":580295817,"identity":"d8e6e4a5-a74e-4f4b-91dd-29e29570198a","order_by":1,"name":"Shintaro 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This study proposes an AI welding-sequence optimization system integrating reinforcement learning with finite-element method (FEM) distortion analysis. The problem is formulated as a sequential decision process where states encode execution history and actions select the next weld line to minimize final deformation. For a four-weld-line specimen, Q-learning is applied and the derived sequences are validated by thermo-elasto-plastic FEM analysis and stereo-vision measurements of out-of-plane displacement and end-angle, confirming good agreement between analysis and experiments. For a ship-block-scale model with 22 weld lines, a Deep Q-Network (DQN) combined with inherent-strain-based FEM enables efficient search and reduces peak out-of-plane displacement from 30~mm to 15~mm. 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