Physics-Informed Neural Network for Real-Time Thermal Analysis and Melt Pool Behavior in Multi-Material Laser Metal Deposition

Physics-Informed Neural Network for Real-Time Thermal Analysis and Melt Pool Behavior in Multi-Material Laser Metal Deposition | 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 Physics-Informed Neural Network for Real-Time Thermal Analysis and Melt Pool Behavior in Multi-Material Laser Metal Deposition Sohini Chowdhury, Vasudevan Muringalath Chandran, Arpana Sarmah, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7513283/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 The Laser Metal Deposition (LMD) operation is characterized by rapid thermal cycles and complex thermal-material interaction. This work focuses on predicting peak melt pool temperature for distinct substrate-powder combinations using a pyrometer sensor and an infra-red camera integrated with the LMD experimental set-up for real-time data extraction and characterization of the bead features. In addition, this study suggest application of dual-model architecture that integrates governing physical laws with data-driven learning approach using PINNs. For the development of PINN model, an extensive experimental investigation was conducted for three different substrates (Al3002, Ti and Mild steel) and five distinct powders (Ti6Al4V, AA2024, SS316L, Cu, and Nitinol). For ensuring physical consistent predictions, governing heat conduction equation along with appropriate boundary conditions are incorporated into the loss functions. Thereafter, the PINN model’s performance was benchmarked against machine learning algorithms namely, Random Forest (RF), Polynomial Regression (PR), Decision Trees (DT), Artificial Neural Networks (ANN). The developed PINN temperature estimated module outperformed all benchmarks with an R 2 of 0.97 and a Mean Squared Error (MSE) of 417.67 K². While, the bead geometry prediction module yielded an R 2 value above 0.96. Based on SHAP analysis, it was identified that scanning speed and beam power were the two most dominant input parameters in LMD process that influences the predictive model performance. This research work was an attempt to emphasize the effectiveness of hybrid approaches that integrates physical principles with machine learning especially in industrial regimes with limited data sets. Laser Metal Deposition Physics-Informed-Neural Network Machine Learning Multi-Material Alloys Thermal Analysis Full Text 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-7513283","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545266628,"identity":"3115c8f4-42e8-4705-8874-01e7b0ea9466","order_by":0,"name":"Sohini 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This work focuses on predicting peak melt pool temperature for distinct substrate-powder combinations using a pyrometer sensor and an infra-red camera integrated with the LMD experimental set-up for real-time data extraction and characterization of the bead features. In addition, this study suggest application of dual-model architecture that integrates governing physical laws with data-driven learning approach using PINNs. For the development of PINN model, an extensive experimental investigation was conducted for three different substrates (Al3002, Ti and Mild steel) and five distinct powders (Ti6Al4V, AA2024, SS316L, Cu, and Nitinol). For ensuring physical consistent predictions, governing heat conduction equation along with appropriate boundary conditions are incorporated into the loss functions. 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