Machine Learning-enhanced optimization of Laser-based Direct Energy Deposition Additive Manufacturing Technology

Machine Learning-enhanced optimization of Laser-based Direct Energy Deposition Additive Manufacturing Technology | 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-enhanced optimization of Laser-based Direct Energy Deposition Additive Manufacturing Technology Runeal Ramma, Carlos Moreira, Michele Chiumenti, Manuel Alejandro Caicedo, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8641262/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-based Direct Energy Deposition (DED-LB) is a key technology in both manufacturing and repair of components within metal Additive Manufacturing (AM). However, challenges such as heat accumulation and insufficient dynamic process control restrict its broader adoption. This work proposes a novel strategy to stabilize the deposition process by integrating a Machine Learning (ML) model as an online controller, coupled with a Finite Element (FE) solver for real-time process monitoring of the deposition process within a computational domain. The FE framework performs thermal analysis of the DED-LB process and generates synthetic data for training the ML model. The ML-enhanced control system then analyzes the melt pool morphology and thermal profiles from simulations in real-time, and outputs corrective laser power adjustments to maintain a constant penetration depth. This closed-loop system enables autonomous monitoring and rapid dynamic response, ensuring consistent thermal management throughout the deposition process. The ML model architecture was optimized through hyperparameter tuning and trained using synthetic data generated from high-fidelity simulations. Three scanning sequence scenarios are presented to evaluate the accuracy of the online monitoring system. Results demonstrate that integrating this control framework maintains a stable melt pool penetration depth, thereby enhancing geometric precision and reliability in DED-LB processes through tailored time-series power profiles. The generalization capability of the ML-based controller was demonstrated by its effective performance on scenarios beyond the training data. This result highlights its adaptability and improved process control compared to traditional parameter-tuned controllers. By improving dimensional accuracy and consistency of AM components, this study supports broader industrial adoption of the DED-LB technology. Additionally, it establishes a preliminary framework for evaluating the feasibility of the proposed control strategy, with the objective of future implementation for physical control of DED-LB machines by adjusting the laser power inreal-time. Full Text Additional Declarations No competing interests reported. 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. 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-8641262","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595175883,"identity":"fa1f2dbf-bd3b-4ff4-b995-5c6748a9d1f4","order_by":0,"name":"Runeal 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However, challenges such as heat accumulation and insufficient dynamic process control restrict its broader adoption. This work proposes a novel strategy to stabilize the deposition process by integrating a Machine Learning (ML) model as an online controller, coupled with a Finite Element (FE) solver for real-time process monitoring of the deposition process within a computational domain. The FE framework performs thermal analysis of the DED-LB process and generates synthetic data for training the ML model. The ML-enhanced control system then analyzes the melt pool morphology and thermal profiles from simulations in real-time, and outputs corrective laser power adjustments to maintain a constant penetration depth. This closed-loop system enables autonomous monitoring and rapid dynamic response, ensuring consistent thermal management throughout the deposition process. The ML model architecture was optimized through hyperparameter tuning and trained using synthetic data generated from high-fidelity simulations. Three scanning sequence scenarios are presented to evaluate the accuracy of the online monitoring system. Results demonstrate that integrating this control framework maintains a stable melt pool penetration depth, thereby enhancing geometric precision and reliability in DED-LB processes through tailored time-series power profiles. The generalization capability of the ML-based controller was demonstrated by its effective performance on scenarios beyond the training data. This result highlights its adaptability and improved process control compared to traditional parameter-tuned controllers. By improving dimensional accuracy and consistency of AM components, this study supports broader industrial adoption of the DED-LB technology. 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