Topology Optimization of Self-supporting Porous Structures Based on Triply Periodic Minimal Surfaces

Zheng, Nan; Zhai, Xiaoya; Chen, Falai

doi:10.1016/j.cad.2023.103542

Cited by 11 publications

(2 citation statements)

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“…33 Finally, regarding self-support problems, Choi et al 34 presented a spheroidal hollow port-free hollowing method that uses an arrangement of vertically aligned spheroids. Recently, Zheng et al 35 proposed an algorithm to design a self-supporting porous structure by integrating overhang constraints (period and thickness variables) in triply minimal surface anatomies. Wang et al 6 generate an adaptive lattice structure by a subdivision method.…”

Section: Introductionmentioning

confidence: 99%

Additive manufacturing of non-uniform scaffolds for mass center control

Robles-Lorite,

Dorado-Vicente,

Medina-Sánchez

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part L:

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In addition to solid components, additive manufacturing can shape parts with internal structures or lattices to control specific mechanical properties such as the relation between strength and mass, inertia, and the center of mass. This study shows a strategy to produce scaffolds based on linear mapping, exponential parameterization, and Voronoi's diagram. The approach provides layer by layer, a net of lines denser on a specific layer point that can be defined to control the layer center of mass. This method is compatible with current CAD software and three-dimensional printing procedure stages; thus, it is easily reproducible. Furthermore, different linear mapping rulings and parameterizations were tested to evaluate the method's capabilities for modifying the center of mass of a symmetrical NACA 0015 profile. The numerical and experimental results show that it is possible to accommodate the center of mass between −8 and 18 mm to the NACA's theoretical center of mass.

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Section: Introductionmentioning

confidence: 99%

Additive manufacturing of non-uniform scaffolds for mass center control

Robles-Lorite,

Dorado-Vicente,

Medina-Sánchez

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part L:

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show abstract

“…Figure4A,B are typical structural diagrams of IWP and BCC lattice with the same volume fraction, respectively. Due to the self-supporting function23,24 of the IWP…”

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confidence: 99%

Customized 3D‐printed heterogeneous porous titanium scaffolds for bone tissue engineering

Fan,

Li,

et al. 2024

MedComm – Biomaterials and Applications

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Bone defect is a common clinical disease. Due to the uncertainty of trauma or infection areas, customized size features are often required for bone substitutes. By inspiration of the natural bone structure, this study designs porous scaffolds with a biomimetic design perspective by using different inner and outer pore units. The outer pore units adopt body‐centered cubic (BCC) structure to simulate the weight‐bearing function of human cortical bone, while inner pore units using I‐Wrapped Package structure, a kind of three periods minimum surface, to obtain a good permeability and simulates the inner layer of cancellous bone. To further regulate the overall modulus of the scaffold within the range of natural bone modulus in the human body, the scaffold was designed to axial gradient structure. Compression experiments were conducted, and the results indicated that when the volume fraction linearly increased from 20% to 50%, the Young's modulus was close to the cortical bone modulus in the human body. In vitro cell experiments further proved that osteoblasts have good cellular activity and spreading morphology on the surface of this scaffold. The customized 3D‐printed heterogeneous porous titanium scaffold has great application potential in bone tissue engineering.

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Density-based topology optimization with the Null Space Optimizer: a tutorial and a comparison

Feppon

2024

Struct Multidisc Optim

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The Null Space Optimizer is a constrained optimization solver that has been developed in the context of level-set based Topology Optimization. One of its appealing aspects comes from its relative independence to the need for tuning unintuitive algorithm parameters. The first contribution of this paper is to introduce an upgrade of the Null Space Optimizer that enables to solve optimization problems featuring a large number of constraints with sparse Jacobian matrix. This allows to include in particular bound constraints, making it possible to use the Null Space Optimizer for solving density based Topology Optimization problems. The second contribution of the paper is to present three tutorials giving an educational view on how to use the open source Python implementation of the Null Space Optimizer for solving Topology Optimization problems in structural mechanics and conductive heat transfer, on structured and unstructured meshes. Elegant Python programming features are used for automating the implementation of density filters and the assembly of sparse Jacobian matrices. Numerical results are presented on three design problems and compared to those obtained with the Method of Moving Asymptotes (MMA), the Interior Point Optimizer IPOPT and the Optimality Criteria (OC) method. We found that on the situations considered, (i) the Null Space optimizer is able to compute optimized designs with performances comparable to its competitors with very little parameter tuning, (ii) the OC method or IPOPT with default parameters sometimes converge to nonoptimal designs, (iii) MMA sometimes converge to slightly better design with a faster decay than the Null Space Optimizer during the first iterations, but may also require case dependent fixes to converge to satisfactory solutions.

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Topology Optimization of Self-supporting Porous Structures Based on Triply Periodic Minimal Surfaces

Cited by 11 publications

References 50 publications

Additive manufacturing of non-uniform scaffolds for mass center control

Additive manufacturing of non-uniform scaffolds for mass center control

Customized 3D‐printed heterogeneous porous titanium scaffolds for bone tissue engineering

Density-based topology optimization with the Null Space Optimizer: a tutorial and a comparison

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