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

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.