A Parameter-Aware Graph Neural Network Framework for Early Design Stage LCA in Additive Manufacturing

A Parameter-Aware Graph Neural Network Framework for Early Design Stage LCA in 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 A Parameter-Aware Graph Neural Network Framework for Early Design Stage LCA in Additive Manufacturing Niccolò Giovenali, Giulia Bruno, Paolo Chiabert, Frédéric Segonds This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7573293/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Mar, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract Additive Manufacturing is a transformative production technology, but its sustainable adoption is hindered by the lack of tools to evaluate the environmental and economic impacts of design decisions during the early conceptual stages. Particularly, the challenge lies in accurately predicting key manufacturing outputs (e.g., print time, material consumption, and support material) early in the design process, translating abstract design choices into tangible sustainability metrics.This paper introduces a Graph Neural Network (GNN) framework that learns directly from the high-fidelity mesh geometry of 3D models and incorporates user-defined printing parameters (e.g., layer height, infill density). The GNN predicts three critical outputs: final part mass, support material mass, and total building time. These predictions form the foundation for a real-time environmental impact assessment based on Life Cycle Assessment principles. By enabling designers to instantly explore the trade-offs between part quality, build speed, and environmental footprint without requiring deep process knowledge, this tool facilitates early-stage optimization, minimizes material waste, and enhances the overall efficiency of the additive manufacturing process.The GNN was trained on a dataset of approximately 60,000 instances derived from the Slice-100K dataset, which includes a wide range of geometries and printing configurations. It demonstrated high predictive accuracy, explaining over 92% of the variance for all three outputs. The model outperformed existing methods in predictive accuracy and flexibility, particularly by enabling dynamic predictions across 81 different printing configurations. Experimental validation on physical 3D-printed parts confirmed the model's practical utility, with acceptable prediction errors for early-stage design. Additive Manufacturing Life Cycle Assessment Graph Neural Network Eco Design Full Text Cite Share Download PDF Status: Published Journal Publication published 30 Mar, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 19 Dec, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers invited by journal 23 Oct, 2025 Editor assigned by journal 30 Sep, 2025 First submitted to journal 26 Sep, 2025 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-7573293","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534138114,"identity":"42b6d9da-7ed1-49a3-ace0-37b1abb35820","order_by":0,"name":"Niccolò 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Particularly, the challenge lies in accurately predicting key manufacturing outputs (e.g., print time, material consumption, and support material) early in the design process, translating abstract design choices into tangible sustainability metrics.This paper introduces a Graph Neural Network (GNN) framework that learns directly from the high-fidelity mesh geometry of 3D models and incorporates user-defined printing parameters (e.g., layer height, infill density). The GNN predicts three critical outputs: final part mass, support material mass, and total building time. These predictions form the foundation for a real-time environmental impact assessment based on Life Cycle Assessment principles. By enabling designers to instantly explore the trade-offs between part quality, build speed, and environmental footprint without requiring deep process knowledge, this tool facilitates early-stage optimization, minimizes material waste, and enhances the overall efficiency of the additive manufacturing process.The GNN was trained on a dataset of approximately 60,000 instances derived from the Slice-100K dataset, which includes a wide range of geometries and printing configurations. It demonstrated high predictive accuracy, explaining over 92% of the variance for all three outputs. The model outperformed existing methods in predictive accuracy and flexibility, particularly by enabling dynamic predictions across 81 different printing configurations. 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