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

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

doi:https://doi.org/10.3390/jimaging9050090

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

Sugiura, Keiya; Ogawa, Toshio; Adachi, Yoshitaka et al.

Journal of Imaging, 2023, 9(5), 90

アクセス数:49件（2025-04-03 21:31 集計）

固定URL: https://hdl.handle.net/11094/93116

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タイトル	Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel
著者	著者名 Sugiura, Keiya
	NRID 1000010708910 1000010708910 著者名 Ogawa, Toshio
	NRID 1000090370311 1000090370311 著者名 Adachi, Yoshitaka
	NRID 1000000836562 1000000836562 著者名 Sun, Fei
	NRID 1000090802603 1000090802603 著者名 Suzuki, Asuka
	NRID 1000050542198 1000050542198 著者名 Yamanaka, Akinori
	NRID 1000050380580 1000050380580 著者名 Nakada, Nobuo
	NRID 1000050508835 1000050508835 著者名 Ishimoto, Takuya
	NRID 1000030243182 1000030243182 ORCID 0000-0001-8052-1698 0000-0001-8052-1698 researchmap permalink read0013987 read0013987 Scopus Author ID 7403896013 7403896013 著者名 Nakano, Takayoshi
	NRID 1000010322174 1000010322174 ORCID 0000-0001-7061-7135 0000-0001-7061-7135 researchmap permalink YuichiroKoizumi YuichiroKoizumi Scopus Author ID 57202772340 57202772340 著者名 Koizumi, Yuichiro
キーワード等	additive manufacturing
	autocorrelation function
	generative adversarial network
	SliceGAN
	synthetic 3D image
内容	Sugiura K., Ogawa T., Adachi Y., et al. Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel. Journal of Imaging 9, 90 (2023); https://doi.org/10.3390/jimaging9050090.
抄録	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.
出版者	MDPI
収録物名	Journal of Imaging
巻(号)	9(5)
ページ	90
刊行年月	2023-04-29
言語	英語
ハンドルURL	https://hdl.handle.net/11094/93116
eISSN	2313433X
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.3390/jimaging9050090
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Journal of Imaging. 2023, 9(5), p. 90
DC.title	Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel
DC.creator	Sugiura, Keiya
	Ogawa, Toshio
	Adachi, Yoshitaka
	Sun, Fei
	Suzuki, Asuka
	Yamanaka, Akinori
	Nakada, Nobuo
	Ishimoto, Takuya
	Nakano, Takayoshi
	Koizumi, Yuichiro
DC.publisher	MDPI
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2023-04-29
DC.identifier	https://doi.org/10.3390/jimaging9050090
DC.subject	additive manufacturing
	autocorrelation function
	generative adversarial network
	SliceGAN
	synthetic 3D image
DC.description	Sugiura K., Ogawa T., Adachi Y., et al. Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel. Journal of Imaging 9, 90 (2023); https://doi.org/10.3390/jimaging9050090.
DCTERMS.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.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	93116
citation_title	Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel
citation_author	Sugiura, Keiya
	Ogawa, Toshio
	Adachi, Yoshitaka
	Sun, Fei
	Suzuki, Asuka
	Yamanaka, Akinori
	Nakada, Nobuo
	Ishimoto, Takuya
	Nakano, Takayoshi
	Koizumi, Yuichiro
citation_publisher	MDPI
citation_language	英語
citation_date	2023-04-29
citation_journal_title	Journal of Imaging
citation_volume	9
citation_issue	5
citation_firstpage	90
citation_doi	https://doi.org/10.3390/jimaging9050090
citation_public_url	https://hdl.handle.net/11094/93116

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

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