Structural Design Using Laplacian Shells

Ulu, Erva; McCann, James; Kara, Levent Burak

doi:10.1111/cgf.13791

Cited by 7 publications

(3 citation statements)

References 59 publications

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“…Existing methods include the density-based approaches such as the solid isotropic material with penalization (SIMP) method [1][2][3][4][5][6][7][8][9][10], grid-based approaches [11][12][13][14][15][16], moving boundary-based approaches [17][18][19][20][21][22][23][24][25], load-path-based approaches [26][27][28], and optimal partitioning approaches [29][30][31]. Although significant efforts have been made to improve solution efficiency, topology optimization methods remain to be computationally demanding and are not readily suited to be used inside other design optimization modules such as layout or configuration design tools [25,32,33].…”

Section: Introductionmentioning

confidence: 99%

TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain

Nie

Lin

Jiang

et al. 2021

Journal of Mechanical Design

138

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In topology optimization using deep learning, the load and boundary conditions represented as vectors or sparse matrices often miss the opportunity to encode a rich view of the design problem, leading to less than ideal generalization results. We propose a new data-driven topology optimization model called TopologyGAN that takes advantage of various physical fields computed on the original, unoptimized material domain, as inputs to the generator of a conditional generative adversarial network (cGAN). Compared to a baseline cGAN, TopologyGAN achieves a nearly 3 × reduction in the mean squared error and a 2.5 × reduction in the mean absolute error on test problems involving previously unseen boundary conditions. Built on several existing network models, we also introduce a hybrid network called U-SE(Squeeze-and-Excitation)-ResNet for the generator that further increases the overall accuracy. We publicly share our full implementation and trained network.

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Section: Introductionmentioning

confidence: 99%

TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain

Nie

Lin

Jiang

et al. 2021

Journal of Mechanical Design

138

View full text Add to dashboard Cite

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“…Laplacian shells were used to optimize structural properties such as strength, stiffness, and weight reduction [22]. Their method modifies the thickness of certain regions, smooths sharp edges, and modifies the overall shape, improving structural properties.…”

Section: Thin Shell Manufacturingmentioning

confidence: 99%

Shell stand: Stable thin shell models for 3D fabrication

Xing,

Wang,

et al. 2024

Comp. Visual Media

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A thin shell model refers to a surface or structure, where the object’s thickness is considered negligible. In the context of 3D printing, thin shell models are characterized by having lightweight, hollow structures, and reduced material usage. Their versatility and visual appeal make them popular in various fields, such as cloth simulation, character skinning, and for thin-walled structures like leaves, paper, or metal sheets. Nevertheless, optimization of thin shell models without external support remains a challenge due to their minimal interior operational space. For the same reasons, hollowing methods are also unsuitable for this task. In fact, thin shell modulation methods are required to preserve the visual appearance of a two-sided surface which further constrain the problem space. In this paper, we introduce a new visual disparity metric tailored for shell models, integrating local details and global shape attributes in terms of visual perception. Our method modulates thin shell models using global deformations and local thickening while accounting for visual saliency, stability, and structural integrity. Thereby, thin shell models such as bas-reliefs, hollow shapes, and cloth can be stabilized to stand in arbitrary orientations, making them ideal for 3D printing.

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“…Recent advances in additive manufacturing technologies have triggered the development of powerful design methodologies allowing designers to create highly complex functional parts [1,2,3,4,5]. Many such methods often operate under the assumption that any designed shape can be fabricated using a 3D printer and the resulting part matches the designed shape perfectly or with negligible errors.…”

Section: Introductionmentioning

confidence: 99%

Manufacturability Oriented Model Correction and Build Direction Optimization for Additive Manufacturing

Ulu

Hsiao

et al. 2019

Journal of Mechanical Design

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We introduce a method to analyze and modify a shape to make it manufacturable for a given additive manufacturing (AM) process. Different AM technologies, process parameters or materials introduce geometric constraints on what is manufacturable or not. Given an input 3D model and minimum printable feature size dictated by the manufacturing process characteristics and parameters, our algorithm generates a corrected geometry that is printable with the intended AM process. A key issue in model correction for manufacturability is the identification of critical features that are affected by the printing process. To address this challenge, we propose a topology aware approach to construct the allowable space for a print head to traverse during the 3D printing process. Combined with our build orientation optimization algorithm, the amount of modifications performed on the shape is kept at minimum while providing an accurate approximation of the as-manufactured part. We demonstrate our method on a variety of 3D models and validate it by 3D printing the results.

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Structural Design Using Laplacian Shells

Cited by 7 publications

References 59 publications

TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain

TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain

Shell stand: Stable thin shell models for 3D fabrication

Manufacturability Oriented Model Correction and Build Direction Optimization for Additive Manufacturing

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