Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel

Keiya Sugiura; Toshio Ogawa; Yoshitaka Adachi; Fei Sun; Asuka Suzuki; Akinori Yamanaka; Nobuo Nakada; Takuya Ishimoto; Takayoshi Nakano; Yuichiro Koizumi

doi:10.3390/jimaging9050090

Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel

J Imaging. 2023 Apr 29;9(5):90. doi: 10.3390/jimaging9050090.

Authors

Affiliations

¹ Department of Material Design Innovation Engineering, Nagoya University, Nagoya 464-8603, Japan.
² Division of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan.
³ School of Materials and Chemical Technology, Tokyo Institute of Technology, Tokyo 226-8503, Japan.
⁴ Department of Materials Design and Engineering, Toyama University, Toyama 930-8555, Japan.
⁵ Division of Materials and Manufacturing Science, Osaka University, Osaka 565-0871, Japan.

Abstract

A modified SliceGAN architecture was proposed to generate a high-quality synthetic three-dimensional (3D) microstructure image of TYPE 316L material manufactured through additive methods. The quality of the resulting 3D image was evaluated using an auto-correlation function, and it was discovered that maintaining a high resolution while doubling the training image size was crucial in creating a more realistic synthetic 3D image. To meet this requirement, modified 3D image generator and critic architecture was developed within the SliceGAN framework.

Keywords: SliceGAN; additive manufacturing; autocorrelation function; generative adversarial network; synthetic 3D image.

Abstract

Grants and funding