Journal of Imaging (Apr 2023)

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
https://doi.org/10.3390/jimaging9050090
Journal volume & issue
Vol. 9, no. 5
p. 90

Abstract

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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.

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