Analytical modeling of materials properties in metal additive manufacturing

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 Analytical modeling of materials properties in metal additive manufacturing Wei Huang, Alireza Fadavi-Boostani, Ruoqi Gao, Navid Nasajpour-Esfahani, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5860271/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) provides a green and sustainable manufacturing approach in an inverse philosophy compared to traditional procedures, benefiting the current global decarbonization strategy. However, AM still needs to address many challenges due to its multi-physical processes in various materials systems or multiple applying situations before implementation in more fields and replacing more places of traditional manufacturing. Specifically, the primary aim of this investigation is to study the microstructural changes that affect material properties, such as elastic modulus and Poisson’s ratio. These changes affect the materials performance, including residual stress, fractures, etc. To achieve this, the characterization of the microstructure of materials, mainly the surface/textures, grain size, and defects, if necessary, is of great importance. In this paper, the texture and grain size simulation for multi-phase materials systems are conducted based on accurate physical stimuli modeling of processing. The influence of microstructural evolution in metal additive manufacturing on material properties is characterized. Several paradigms have been constructed to model and predict manufacturing processes, utilizing physics-based semi-analytical frameworks. Experimental results have been presented to validate the fidelity of the models. It has been observed that the computer-simulated effective elastic modulus, using the same experimental processing parameters, is relatively stable and falls within the range of 109-117 GPa, showing no evidence of being influenced by layer or row settings. Besides, the simulated derivatives under the same settings are stable at around 850-900 MPa, falling within the range of experimental data of approximately 850-1050 MPa, albeit slightly lower. By bridging the gap between AM’s micro- and macrostructures and material properties, this work has the potential to transform the sector and spark a fresh outlook on science. Additive manufacturing Process-structure-properties Analytical modeling Anisotropy Texture Grain size Elastic Modulus Yield strength 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-5860271","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435425302,"identity":"fe58c7a7-7e0a-4d6f-b7c6-fa766c827966","order_by":0,"name":"Wei Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDACCTBZU9/P3gBmJQCxATFajjHO7DnAwHCABC3MjBtuJBCpxeB2j+Hngl9szJIzn1/8/KHCLo+BvXmbBF4td84YS8/sk2Hjl84pljhwJrmYgedYGV4tZjdyDKR5e9h4JGfnJEgcbDuQ2CCRY0ZIi/Fv3h5mCYObZ5J/HPwH1CL/hqAWM2meH8wGBjfYj0kcbADZwoNfi/2NtDJr3oZjCZI9OWwWZ44lJ7bxpBVb4NMiOSN5822ePzUJ/OzHH9+oqLFL7Gc/vPEGPi1gwNgGInkg0cFGUDkY/AER7A+IUzwKRsEoGAUjDgAAiflQHRSC+dIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9654-6002","institution":"Georgia Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Huang","suffix":""},{"id":435425303,"identity":"22fc3695-2b06-48df-85d9-ea1b87c6ab24","order_by":1,"name":"Alireza Fadavi-Boostani","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Fadavi-Boostani","suffix":""},{"id":435425304,"identity":"f48c3916-6bd1-4535-8f13-43e7d9f9eba9","order_by":2,"name":"Ruoqi Gao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ruoqi","middleName":"","lastName":"Gao","suffix":""},{"id":435425305,"identity":"ae1fb4e0-9d23-4df7-a980-566206bf4203","order_by":3,"name":"Navid Nasajpour-Esfahani","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Navid","middleName":"","lastName":"Nasajpour-Esfahani","suffix":""},{"id":435425306,"identity":"28cd53e1-0756-47f8-a1ef-7359667931b5","order_by":4,"name":"Hamid Garmestani","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Garmestani","suffix":""},{"id":435425307,"identity":"9d7e95fe-cd33-4211-96f1-cedd37bc0c28","order_by":5,"name":"Steven Y Liang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Steven","middleName":"Y","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2025-01-19 15:36:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5860271/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5860271/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84130627,"identity":"3424cceb-36ae-44fb-87f0-33b4427945f7","added_by":"auto","created_at":"2025-06-07 14:18:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1208068,"visible":true,"origin":"","legend":"","description":"","filename":"Paper4revised1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5860271/v1_covered_716028d5-57b1-4ee9-8f51-9226ef96c75a.pdf"}],"financialInterests":"","formattedTitle":"Analytical modeling of materials properties in metal additive manufacturing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Additive manufacturing, Process-structure-properties, Analytical modeling, Anisotropy, Texture, Grain size, Elastic Modulus, Yield strength","lastPublishedDoi":"10.21203/rs.3.rs-5860271/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5860271/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Emerging additive manufacturing (AM) provides a green and sustainable manufacturing approach in an inverse philosophy compared to traditional procedures, benefiting the current global decarbonization strategy. 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