Physics-based analytical modeling of materials properties in metal additive manufacturing

Physics-based analytical modeling of materials properties in metal 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 Physics-based analytical modeling of materials properties in metal additive manufacturing Wei Huang, Ruoqi Gao, Hamid Garmestani, Steven Y. Liang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6925990/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 Emerging additive manufacturing (AM) offers a sustainable alternative to the subtractive processes with significant potential for complex geometries and material efficiency. However, predicting and controlling the microstructure-dependent properties of AM parts, particularly metals, remains challenging due to complex multi-physical processes. This work develops a physics-based analytical modeling framework to predict the evolution of key microstructural features (texture and grain size) and their influence on material properties (elastic modulus, Poisson’s ratio, yield strength) in laser powder bed fusion (LPBF) of Ti-6Al-4V. The framework integrates: (1) a 3D thermal profile model with boundary heat transfer for a moving point heat source; (2) Johnson-Mehl-Avrami-Kolmogorov (JMAK) kinetics and Green’s function-based thermal stress analysis for grain size prediction during heating and cooling; (3) columnar-to-equiaxed transition (CET) criteria and Bunge calculation for multi-phase texture evolution; (4) a self-consistent model to predict texture-affected anisotropic elastic modulus and Poisson’s ratio; and (5) the Hall-Petch relation for grain size-dependent yield strength. Experimental validations confirm the fidelity of the thermal model (molten pool dimensions), texture simulation (pole figure intensities), and predicted properties. Crucially, the simulated effective elastic modulus (109-117 GPa) and yield strength (850-900 MPa) under consistent processing parameters align well with experimental ranges (100-140 GPa and 850-1050 MPa, respectively) and show stability regardless of layer or row settings. The Poisson’s ratio exhibits significant anisotropy (approx. 0.45-0.5 in X/Y vs. lower values in other directions). By bridging processing parameters, microstructure evolution, and final properties, this work provides a paradigm for computationally efficient prediction and optimization of AM material performance, paving the way for inverse design strategies. Additive manufacturing Process-structure-properties Analytical modeling Anisotropy Texture Grain size Elastic Modulus Yield strength Full Text Additional Declarations The authors declare no competing interests. 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-6925990","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473281083,"identity":"949b822e-447a-4ef7-8af2-1f3cea77b7d4","order_by":0,"name":"Wei Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYFACxgZmMM3eAyQOMMiAOTxEaeE5A9bCQ4QWBgaIFokcIrUYHG9uYC5ss8uTj3x78HHFGRsefokExgdv2/BoOXOwgXlmW3Kx4e28ZMMzN9J4JGckMBvOxaPF7EZiAzNvG3Pixtk5ZpINHw7zGNxIYJPmxafl/kOQlvrEjTPPmP9s+PCfx/5GAvtvvFpuMIK0HE6cL8Fjxthw4wCPgUQCGzM+LfZngA7jOXc8cQNPXrJkw5lkHokzD5sl55zDrUWy/fgDZp6y6sT57WcPfmw4ZifH35588MObMtxagID9B4g0OADjCyQ24FUPB/JwdfwHcKsaBaNgFIyCEQkATvpVeRkiUwMAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Huang","suffix":""},{"id":473281084,"identity":"c22c728e-794b-41fc-b1da-35163d3468d4","order_by":1,"name":"Ruoqi Gao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ruoqi","middleName":"","lastName":"Gao","suffix":""},{"id":473281085,"identity":"6e0895bf-8de8-4f4a-b85e-3307e00d7d54","order_by":2,"name":"Hamid Garmestani","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Garmestani","suffix":""},{"id":473281086,"identity":"27b2ac95-1c33-40f2-b51b-d03e4f25efd3","order_by":3,"name":"Steven Y. 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The framework integrates: (1) a 3D thermal profile model with boundary heat transfer for a moving point heat source; (2) Johnson-Mehl-Avrami-Kolmogorov (JMAK) kinetics and Green’s function-based thermal stress analysis for grain size prediction during heating and cooling; (3) columnar-to-equiaxed transition (CET) criteria and Bunge calculation for multi-phase texture evolution; (4) a self-consistent model to predict texture-affected anisotropic elastic modulus and Poisson’s ratio; and (5) the Hall-Petch relation for grain size-dependent yield strength. Experimental validations confirm the fidelity of the thermal model (molten pool dimensions), texture simulation (pole figure intensities), and predicted properties. Crucially, the simulated effective elastic modulus (109-117 GPa) and yield strength (850-900 MPa) under consistent processing parameters align well with experimental ranges (100-140 GPa and 850-1050 MPa, respectively) and show stability regardless of layer or row settings. 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