Graph Neural Network-Based Topology Optimization for Efficient Support Structure Design in Additive Manufacturing

Graph Neural Network-Based Topology Optimization for Efficient Support Structure Design 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 Graph Neural Network-Based Topology Optimization for Efficient Support Structure Design in Additive Manufacturing Saquib Ahmad Bhuiyan, Alireza Tabarraei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6567604/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Topology optimization (TO) has emerged as a powerful tool for designing high-performance structures by optimizing material distribution to satisfy specific performance criteria. While traditional TO methods are widely applied in industries such as aerospace, automotive, and architecture, their high computational costs pose significant challenges, especially in large-scale problems with complex constraints. These limitations are particularly critical in additive manufacturing (AM), where the design of support structures plays a crucial role in minimizing deformation, reducing material waste, and enhancing production efficiency. Recent advancements in deep learning offer a pathway to overcome these challenges by improving computational efficiency without sacrificing accuracy. This paper focuses on a novel graph neural network (GNN)-based topology optimization framework specifically designed for the creation of support structures in AM. By representing the design domain as a graph, where nodes correspond to material elements and edges capture their spatial relationships, the GNN effectively models the complex geometries of support structures. A Fourier projection layer is incorporated to enhance the resolution of fine structural details, ensuring the generation of precise and efficient support designs. The optimization process is guided by a hybrid loss function that combines compliance minimization with optimization constraints. A key feature of the proposed framework is the seamless integration of finite element analysis (FEA) within the GNN architecture, allowing for an efficient sensitivity analysis through automatic differentiation. Numerical results demonstrate the effectiveness of the method in generating optimized support structures that minimize deformation under applied forces, reduce material usage, and shorten the design-to-manufacture pipeline. Topology optimization Deep learning Neural network Support Structure Additive Manufacturing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Jun, 2025 Reviews received at journal 17 Jun, 2025 Reviews received at journal 01 Jun, 2025 Reviewers agreed at journal 25 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers invited by journal 15 May, 2025 Editor assigned by journal 02 May, 2025 Submission checks completed at journal 02 May, 2025 First submitted to journal 30 Apr, 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. 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