Generating 3D voxelized architected materials using 3D conditional generative adversarial network

Xiaoyang Zheng; Ikumu Watanabe

doi:10.1080/14686996.2022.2157682

ソフトウェア Generating 3D voxelized architected materials using 3D conditional generative adversarial network

Xiaoyang Zheng (Center for Basic Research on Materials, National Institute for Materials Science) ; Ikumu Watanabe (Center for Basic Research on Materials, National Institute for Materials Science)

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Xiaoyang Zheng, Ikumu Watanabe. Generating 3D voxelized architected materials using 3D conditional generative adversarial network. https://doi.org/10.1080/14686996.2022.2157682

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(abstract)

This tutorial aims to give an introduction of how to use a deep generative model, 3D conditional generative adversarial network (3D-CGAN). The 3D-CGAN can be used for the inverse design of 3D voxelized microstructures with target properties. The 3D-CGAN is trained with supervised learning using a labeled dataset. The dataset consists of a large number of geometries (3D arrays) and their corresponding properties (e.g., elastic moduli). After training, the 3D-CGAN can generate a batch of geometries using target properties at inputs. In our previous tutorial, we have demonstrated how to use CGAN for the inverse design of 2D microstructures. This work is based on our previous publication for the inverse design of 3D architected materials. We hope this tutorial can be useful for those who are interested in the inverse design problems of microstructures.

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