On Machine Learning Control in LPBF Additive Manufacturing

On Machine Learning Control in LPBF Additive 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 On Machine Learning Control in LPBF Additive Manufacturing Jan Boer, Marcin Magolon, Mohamed Abdelaziz Elbestawi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8244687/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 Laser Powder Bed Fusion (LPBF) remains constrained by the absence of closed-loop control systems capable of preventing porosity and cracking during fabrication. This study introduces a physics-informed, feed-forward control framework that integrates synchronized acoustic emission and pyrometer data for defect detection and predictive process adjustment. Machine learning models trained on induced and natural defect datasets achieved up to 95% classification accuracy and produced consistent thermal responses across multiple alloys and build geometries. The framework combines variational autoencoders with physics-based control logic to enable near real-time monitoring and adaptive parameter optimization. The results demonstrate a viable pathway toward scalable, data-driven control in LPBF, advancing the development of autonomous additive manufacturing systems. Additive Manufacturing Process Optimization Machine Learning Control Laser Powder Bed Fusion (LPBF) Acoustic Emission Monitoring Pyrometer-Based Thermal Analysis Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revisions Needed 22 Mar, 2026 Reviewers agreed at journal 10 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor assigned by journal 08 Dec, 2025 First submitted to journal 04 Dec, 2025 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-8244687","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557051776,"identity":"d7ca2372-b008-4816-9504-9fc1a34bdd25","order_by":0,"name":"Jan 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