A Digital Twin Approach for Agile Additive Manufacturing of Automotive Components

A Digital Twin Approach for Agile Additive Manufacturing of Automotive Components | 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 A Digital Twin Approach for Agile Additive Manufacturing of Automotive Components Chinmai Bhat, Mayur Jiyalal Prajapati, Yulius Shan Romario, Wojciech Macek, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3865454/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract This study aims to develop a digital twin framework for fabricating automotive components through additive manufacturing (AM) technology. The framework comprises topology optimization (TO), finite element analysis (FEA), and fabrication analysis using Simufact Additive, which ensures the first-time-right fabrication of the component. Using TO-FEA, the component is designed with reduced overall weight without compromising the structural and functional performance. After the successful design of the component, it is analyzed for fabrication feasibility before undergoing the actual fabrication process. In the present study, an automotive flange fork is designed and fabricated through AM laser powder-bed fusion technology using Inconel-718 material. The optimized process parameters of 180 W laser power, 600 mm/s scan speed, 100 µm hatch spacing, and 30 µm layer thickness were used for the fabrication of the flange. The Simufact analysis revealed that the optimized design's out-of-tolerance was reduced from 18% to 6.1%. In addition, the surface deviation of the optimized design was increased by 2%, compared to the original design. Furthermore, the optimized design showed better thermal characteristics, resulting in reduced residual stress and distortion. Precision analysis of the component showed the dimensional discrepancy between the 3D model and printed part to be less than 1%. The fabricated component showed it can withstand a torque of 300 Nm, which is generated in 4-wheel automobiles. The torque analysis successfully demonstrated the component’s performance, showing no signs of damage or cracks. This study shows the potential of integrating digital twins and additive manufacturing to promote Sustainable Development Goals (SDG) goals across many industries. Physical sciences/Engineering/Mechanical engineering Physical sciences/Engineering/Aerospace engineering Digital twin additive manufacturing topology optimization Laser-powder bed fusion finite element analysis Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. 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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-3865454","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268531606,"identity":"b3b1b6fc-4260-4412-b530-bbcb7b021021","order_by":0,"name":"Chinmai Bhat","email":"","orcid":"","institution":"Department of Mechanical Engineering, National Taipei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Chinmai","middleName":"","lastName":"Bhat","suffix":""},{"id":593264413,"identity":"c5fd6727-586e-41c1-9941-55ae0b2df7a2","order_by":1,"name":"Mayur Jiyalal 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