Machine learning for molten pool dynamics prediction in laser manufacturing

Machine learning for molten pool dynamics prediction in laser manufacturing | 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 Machine learning for molten pool dynamics prediction in laser manufacturing Le Song, Wenbo Zhou, Xuyang Chen, Linlin Jia, Zhiyong Huang, Baorui Du This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8630425/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract This study investigates the influence of line energy density, power and scanning speed on the molten pool geometry and maximum solute concentration obtained under steady-state conditions in hybrid laser–MIG welding of aluminum alloys. A comprehensive numerical model incorporating multiple reflections, Fresnel absorption, and laser–arc coupling was established to simulate the thermo-fluid behavior during the welding process. Key characteristics, including molten pool width, height, depth, and magnesium concentration, were quantitatively analyzed. To enhance predictive capability and improve process optimization efficiency, the Support Vector Regression (SVR) method was introduced to develop a machine learning model. The results demonstrate that SVR effectively captures the nonlinear relationship between line energy density, power, scanning speed and molten pool responses, achieving high predictive accuracy across multiple target variables and providing a reliable basis for process parameter optimization and weld quality improvement. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 26 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviews received at journal 12 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 19 Jan, 2026 Submission checks completed at journal 19 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-8630425","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577630829,"identity":"f461b816-b112-44a1-8306-313b82c8df74","order_by":0,"name":"Le Song","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Song","suffix":""},{"id":577630831,"identity":"2b496cb8-06cd-4699-a138-1cfa1f0eec62","order_by":1,"name":"Wenbo Zhou","email":"","orcid":"","institution":"National University of 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