Download file (6.93MB)
Download as zip (6.28MB)

コレクション

引用

説明:

Architected cellular materials are a class of artificial materials with cellular architecture-dependent properties. Typically, designing cellular architectures paves the way to generate architected cellular materials with specific properties. However, most previous studies have primarily focused on a forward design strategy, wherein a geometry is generated using computer-aided design modeling, and its properties are investigated experimentally or via simulations. In this study, we developed an inverse design framework for a disordered architected cellular material (Voronoi lattices) using deep learning. This inverse design framework is a three-dimensional conditional generative adversarial network (3D-CGAN) trained based on supervised learning using a dataset consisting of voxelized Voronoi lattices and their corresponding relative densities and Young's moduli. A well-trained 3D-CGAN adopts variational sampling to generate multiple distinct Voronoi lattices with the target relative density and Young's modulus. Consequently, the mechanical properties of the 3D-CGAN generated Voronoi lattices are validated through uniaxial compression tests and finite element simulations. The inverse design framework demonstrates potential for use in bone implants, where scaffold implants can be automatically generated with the target relative density and Young's modulus.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

  Creative Commons BY Attribution 4.0 International

キーワード: Architected materials, inverse design, generative adversarial network, mechanical properties, finite element simulation, Voronoi lattices

刊行年月日: 2023-12-31

出版者: Informa UK Limited

掲載誌:

• SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS (ISSN: 14686996) vol. 24 issue. 1 p. 111-125

研究助成金:

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/14686996.2022.2157682

関連資料:

その他の識別子:

連絡先:

更新時刻: 2024-01-05 22:11:52 +0900

MDRでの公開時刻: 2023-02-28 11:18:24 +0900