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Design of Lattice Structures Using Local Relative Density Mapping Method

Guo-Hua Song et al · KeAi Communications Co., Ltd · 2018

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Abstract In order to solve the problem of substantial computational resources of lattice structure during optimization, a local relative density mapping (LRDM) method is proposed. The proposed method uses solid isotropic microstructures with penalization to optimize a model at the macroscopic scale. The local relative density information is obtained from the topology optimization result. The contour lines of an optimized model are extracted using a density contour approach, and the triangular mesh is generated using a mesh generator. A local mapping relationship between the elements’ relative density and the struts’ relative cross-sectional area is established to automatically determine the diameter of each individual strut in the lattice structures. The proposed LRDM method can be applied to local finite element meshes and local density elements, but it is also suitable for global ones. In addition, some cases are considered in order to test the effectiveness of the LRDM method. The results show that the solution time of the LRDM is lower than the RDM method by approximately 50%. The proposed method provides instructions for the design of more complex lattice structures.

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APA 7

al, G. H. S. E. (2018). Design of Lattice Structures Using Local Relative Density Mapping Method. https://doi.org/10.1186/s10033-018-0289-3

MLA

al, Guo-Hua Song et. "Design of Lattice Structures Using Local Relative Density Mapping Method." 2018. https://doi.org/10.1186/s10033-018-0289-3.

Chicago

al, Guo-Hua Song et. 2018. "Design of Lattice Structures Using Local Relative Density Mapping Method.". https://doi.org/10.1186/s10033-018-0289-3.

Harvard

al, G. H. S. E. 2018, Design of Lattice Structures Using Local Relative Density Mapping Method, KeAi Communications Co, Ltd, available at: https://doi.org/10.1186/s10033-018-0289-3 [Accessed 8 Aug. 2026].

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Title
Design of Lattice Structures Using Local Relative Density Mapping Method
Author / contributors
Guo-Hua Song et al
Publisher
KeAi Communications Co., Ltd
Publication year
2018
ISSN
1000-9345
ISSN
1000-9345
Language
English

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