Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel
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 th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/9/5/90 |
| _version_ | 1797599611748089856 |
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| author | Keiya Sugiura Toshio Ogawa Yoshitaka Adachi Fei Sun Asuka Suzuki Akinori Yamanaka Nobuo Nakada Takuya Ishimoto Takayoshi Nakano Yuichiro Koizumi |
| author_facet | Keiya Sugiura Toshio Ogawa Yoshitaka Adachi Fei Sun Asuka Suzuki Akinori Yamanaka Nobuo Nakada Takuya Ishimoto Takayoshi Nakano Yuichiro Koizumi |
| author_sort | Keiya Sugiura |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:36:42Z |
| format | Article |
| id | doaj.art-aa67de21008e4d248fa0c467dca30d0c |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-11T03:36:42Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-aa67de21008e4d248fa0c467dca30d0c2023-11-18T01:57:23ZengMDPI AGJournal of Imaging2313-433X2023-04-01959010.3390/jimaging9050090Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L SteelKeiya Sugiura0Toshio Ogawa1Yoshitaka Adachi2Fei Sun3Asuka Suzuki4Akinori Yamanaka5Nobuo Nakada6Takuya Ishimoto7Takayoshi Nakano8Yuichiro Koizumi9Department of Material Design Innovation Engineering, Nagoya University, Nagoya 464-8603, JapanDepartment of Material Design Innovation Engineering, Nagoya University, Nagoya 464-8603, JapanDepartment of Material Design Innovation Engineering, Nagoya University, Nagoya 464-8603, JapanDepartment of Material Design Innovation Engineering, Nagoya University, Nagoya 464-8603, JapanDepartment of Material Design Innovation Engineering, Nagoya University, Nagoya 464-8603, JapanDivision of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, JapanSchool of Materials and Chemical Technology, Tokyo Institute of Technology, Tokyo 226-8503, JapanDepartment of Materials Design and Engineering, Toyama University, Toyama 930-8555, JapanDivision of Materials and Manufacturing Science, Osaka University, Osaka 565-0871, JapanDivision of Materials and Manufacturing Science, Osaka University, Osaka 565-0871, JapanA 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.https://www.mdpi.com/2313-433X/9/5/90SliceGANgenerative adversarial networksynthetic 3D imageadditive manufacturingautocorrelation function |
| spellingShingle | Keiya Sugiura Toshio Ogawa Yoshitaka Adachi Fei Sun Asuka Suzuki Akinori Yamanaka Nobuo Nakada Takuya Ishimoto Takayoshi Nakano Yuichiro Koizumi Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel Journal of Imaging SliceGAN generative adversarial network synthetic 3D image additive manufacturing autocorrelation function |
| title | Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel |
| title_full | Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel |
| title_fullStr | Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel |
| title_full_unstemmed | Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel |
| title_short | Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel |
| title_sort | big volume slicegan for improving a synthetic 3d microstructure image of additive manufactured type 316l steel |
| topic | SliceGAN generative adversarial network synthetic 3D image additive manufacturing autocorrelation function |
| url | https://www.mdpi.com/2313-433X/9/5/90 |
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