X-ray and neural network based in-situ identification of the melt pool during the additivemanufacturing of a stainless steel part

X-ray and neural network based in-situ identification of the melt pool during the additivemanufacturing of a stainless steel part | 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 X-ray and neural network based in-situ identification of the melt pool during the additivemanufacturing of a stainless steel part Loïc JEGOU, Thomas Elguedj, Valerie Kaftandjian, Philippe Duvauchelle, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6293661/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Laser Metal Deposition with Powder (LMDp) is an additive manufacturing techniqueused for repairing metal components or producing parts with intricate geometries.However, a comprehensive understanding of the melt pool dynamics, whichsignificantly influences the final properties of LMDp-fabricated parts, remains limited.Non-destructive testing is highly valuable for conducting in-situ controls duringmanufacturing. X-ray imaging offers the ability to penetrate metallic parts and detectdefects such as porosity. In the context of additive manufacturing, X-rays can beemployed to visualize the shape of the melt pool during the fabrication process. Thecontrast between the liquid and solid phases, due to their density differences, shouldbe observable in the radioscopy images.The experimental setup required to perform such a test on an industrial additivemanufacturing installation consists of a movable X-ray source that producespolychromatic beams, a detector, and extensive lead shielding to ensure X-ray safety.In-situ observations of the melt pool were conducted during the deposition of tensuccessive layers of stainless steel 316L (SS316L). The polychromatic nature of the X-ray beam, however, rendered traditional image analysis methods ineffective fordetecting contrast variations. To address this challenge, neural networks trained onsimulated data (thermal and X-ray) were employed, providing a solution to identify themelt pool in low-contrast radioscopic images. The architecture inspired by VGG16demonstrated promising results, confirming the potential for in-situ non-destructivetesting using X-ray imaging in industrial additive manufacturing processes. Additive manufacturing Direct Energy Deposition Stainless steel X-ray Melt pool Convolutional neural networks Full Text Additional Declarations No competing interests reported. Supplementary Files graphicalabstract.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-6293661","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434246749,"identity":"10842fb0-c83f-417c-97fa-9d122e047841","order_by":0,"name":"Loïc 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To address this challenge, neural networks trained onsimulated data (thermal and X-ray) were employed, providing a solution to identify themelt pool in low-contrast radioscopic images. 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