# Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices

https://mdr.nims.go.jp/datasets/3839dd69-3cc5-4334-8327-a9e835e6f321

## File

- [zheng_STAM2023.pdf](https://mdr.nims.go.jp/filesets/aac11a57-13e9-4e65-8c2f-99f5ef46729e/download) ([Detail](https://mdr.nims.go.jp/filesets/aac11a57-13e9-4e65-8c2f-99f5ef46729e.md))

## Id

3839dd69-3cc5-4334-8327-a9e835e6f321

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-02-16T04:38:31.191931Z

## Updated at

2024-01-05T13:11:52.788388Z

## Published at

2023-02-28T02:18:24.897371Z

## Doi



## First published url

https://doi.org/10.1080/14686996.2022.2157682

## Date published

2023-12-31

## Recorded date published

2023-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Deep-learning-based inverse design of three-dimensional architected cellular
    materials with the target porosity and stiffness using voxelized Voronoi lattices
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: eng

## Creator

- name: Xiaoyang Zheng
  role: author
- name: Ta-Te Chen
  role: author
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Xiaoyu Jiang
  role: author
- name: Masanobu Naito
  role: author
  orcid: https://orcid.org/0000-0001-7198-819X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Ikumu Watanabe
  role: author
  orcid: https://orcid.org/0000-0002-7693-1675
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: Architected materials
  schema: not_defined
- subject: inverse design
  schema: not_defined
- subject: generative adversarial network
  schema: not_defined
- subject: mechanical properties
  schema: not_defined
- subject: finite element simulation
  schema: not_defined
- subject: Voronoi lattices
  schema: not_defined

## Rights

- description: Creative Commons BY Attribution 4.0 International
  identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



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## Journal

- title: SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS
  issn: '14686996'
  volume: '24'
  issue: '1'
  start_page: 111
  end_page: 125

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## Fileset

- id: aac11a57-13e9-4e65-8c2f-99f5ef46729e
  filename: zheng_STAM2023.pdf
  content_type: application/pdf
  size: 7270085
  md5: f8324b8061b08a8eb26d89c49b8260e9

## Thumbnail

fileset_id: aac11a57-13e9-4e65-8c2f-99f5ef46729e
filename: zheng_STAM2023.pdf