4.6 Article

A boundary density evolutionary topology optimization of continuum structures with smooth boundaries

出版社

WILEY
DOI: 10.1002/nme.6851

关键词

boundary density evolution; continuum structures; material interpolation; smooth boundaries; topology optimization

资金

  1. National Natural Science Foundation of China [12072114, 51678252]
  2. Opening Project of Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, South China University of Technology, China [2020kfkt03]

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This article introduces a boundary density evolutionary topology optimization method, which utilizes a material interpolation model and density grading filtering to achieve an optimal topology with only 0/1 cells. Compared to other methods, it eliminates the need for a penalty factor and allows for intermediate-density elements while still removing low-utilization materials.
Based on the variable density method, this article proposes a boundary density evolutionary topology optimization method. The method uses a material interpolation model without penalization. Combined with the density grading filtering method, an optimal topology with only 0/1 cells can be obtained. Compared with the solid isotropic microstructures with penalization method (SIMP), no penalty factor is required in the material interpolation model; compared with the evolutionary structural optimization method (ESO), intermediate-density elements are allowed in the optimization process, but the concept of gradually removing the low-utilization materials near the boundary in the ESO method is retained. After the optimal result is obtained, the structural boundary element is processed by the level set of nodal strain energy, and the optimization result with smooth boundaries similar to the level set method (LSM) can be obtained. The proposed method has the superiority of the variable density method, and it also combines the advantages of the evolutionary method and the level set method, so which is named as boundary density evolution (BDE) method. The four static and one dynamic optimization examples illustrate the stability and efficiency of the proposed method.

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