Browsing by Author "Wang, Shengfa"

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    Continuous Representation based Internal Self-supporting Structure via Ellipsoid Hollowing for 3D Printing
    (The Eurographics Association, 2024) Wang, Shengfa; Yang, Jun; Hu, Jiangbei; Lei, Na; Luo, Zhongxuan; Liu, Ligang; Chen, Renjie; Ritschel, Tobias; Whiting, Emily
    Hollowing is an effective way to achieve lightweight objectives by removing material from the interior volume while maintaining feasible mechanical properties. However, hollowed models often necessitate the use of additional support materials to prevent collapse during the printing process, which can substantially negate the benefits of weight reduction. We introduce a framework for designing and optimizing self-supporting infill cavities, which are represented and optimized directly using continuous functions based on ellipsoids. Ellipsoids are favored as filling structures due to their advantageous properties, including their self-supporting nature, precise mathematical definability, variable controllability, and stress concentration mitigation capabilities. Thanks to the explicit definability, we formulate the creation of self-supporting infill cavities as a structural stiffness optimization problem using function representations. The utilization of function representation eliminates the necessity for remeshing to depict structures and shapes, thereby enabling the direct computation of integrals and gradients on the functions. Based on the representations, we propose an efficient optimization strategy to determine the shapes, positions, and topology of the infill cavities, with the goal of achieving multiple objectives, including minimizing material cost, maximizing structural stiffness, and ensuring self-supporting. We perform various experiments to validate the effectiveness and convergence of our approach. Moreover, we demonstrate the self-supporting and stability of the optimized structures through actual 3D printing trials and real mechanical testing.
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    An Efficient Self-supporting Infill Structure for Computational Fabrication
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Wang, Shengfa; Liu, Zheng; Hu, Jiangbei; Lei, Na; Luo, Zhongxuan; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    Efficiently optimizing the internal structure of 3D printing models is a critical focus in the field of industrial manufacturing, particularly when designing self-supporting structures that offer high stiffness and lightweight characteristics. To tackle this challenge, this research introduces a novel approach featuring a self-supporting polyhedral structure and an efficient optimization algorithm. Specifically, the internal space of the model is filled with a combination of self-supporting octahedrons and tetrahedrons, strategically arranged to maximize structural integrity. Our algorithm optimizes the wall thickness of the polyhedron elements to satisfy specific stiffness requirements, while ensuring efficient alignment of the filled structures in finite element calculations. Our approach results in a considerable decrease in optimization time. The optimization process is stable, converges rapidly, and consistently delivers effective results. Through a series of experiments, we have demonstrated the effectiveness and efficiency of our method in achieving the desired design objectives