Image-Based Machine Learning of In Situ Captured Images of Die-Workpiece Interface in Forging

Matsumoto, Ryo; Kunisawa, Shusuke; Nakamura, Yusaku; Utsunomiya, Hiroshi

doi:https://doi.org/10.4028/p-ex7ohs

Image-Based Machine Learning of In Situ Captured Images of Die-Workpiece Interface in Forging

Matsumoto, Ryo; Kunisawa, Shusuke; Nakamura, Yusaku et al.

Defect and Diffusion Forum, 2025, 441(9), 39-43

アクセス数:504件（2025-08-28 17:24 集計）

固定URL: https://hdl.handle.net/11094/100688

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タイトル	Image-Based Machine Learning of In Situ Captured Images of Die-Workpiece Interface in Forging
著者	NRID 1000050362645 1000050362645 ORCID 0000-0002-4477-2139 0000-0002-4477-2139 researchmap permalink ryo-matsumoto ryo-matsumoto Scopus Author ID 35512063000 35512063000 著者名 Matsumoto, Ryo
	著者名 Kunisawa, Shusuke
	著者名 Nakamura, Yusaku
	NRID 1000080252584 1000080252584 researchmap permalink read0042799 read0042799 Scopus Author ID 7102139699 7102139699 著者名 Utsunomiya, Hiroshi
キーワード等	Forging
	Extrusion
	Lubrication
	Image analysis
	Machine learning
内容	Matsumoto Ryo, Kunisawa Shusuke, Nakamura Yusaku, et al. Image-Based Machine Learning of In Situ Captured Images of Die-Workpiece Interface in Forging. Defect and Diffusion Forum 441, 39 (2025); https://doi.org/10.4028/p-ex7ohs.
抄録	The lubricant thickness in cold forging was estimated by machine learning of the in situ captured images of the die–workpiece contact interface. The images were in situ captured by a high-speed camera from the backside of the transparent glass die during forging of commercially pure aluminum workpiece. On the other hand, the images of the lubricated workpiece were individually captured as training images for random forest with classification. The classification accuracy of the lubricant thickness was confirmed to be approximately 75% (classification ability: 5–10 μm in lubricant thickness) in the training images with 22,500 px (50 px/mm). The in situ captured images of the die–workpiece contact interface during forging were classified by random forest using the training images. The estimated lubricant thickness of the in situ captured image almost agreed with the lubricant thickness estimated from the mean brightness value of the in situ captured image.
会議情報	会議名 the International Conference on Tribology in Manufacturing Processes（ICTMP）回次 10 開催期間 June 26th to 28th, 2024 開催会場 Universitat Politècnica de València 開催地 Alcoy 開催国スペイン
出版者	Trans Tech Publications, Ltd.
収録物名	Defect and Diffusion Forum
巻(号)	441(9)
ページ	39-43
刊行年月	2025-03-26
言語	英語
ハンドルURL	https://hdl.handle.net/11094/100688
eISSN	16629507
関連情報 (isVersionOf)	DOI (出版社版) https://doi.org/10.4028/p-ex7ohs
アクセス権	open access
出版タイプ	Author’s Original
カテゴリ	会議発表論文 Conference Paper

資源タイプ	会議発表論文
ローカル資源タイプ	会議発表論文
DCTERMS.bibliographicCitation	Defect and Diffusion Forum. 2025, 441(9), p. 39-43
DC.title	Image-Based Machine Learning of In Situ Captured Images of Die-Workpiece Interface in Forging
DC.creator	Matsumoto, Ryo
	Kunisawa, Shusuke
	Nakamura, Yusaku
	Utsunomiya, Hiroshi
DC.publisher	Trans Tech Publications, Ltd.
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-03-26
DC.identifier	https://doi.org/10.4028/p-ex7ohs
DC.subject	Forging
	Extrusion
	Lubrication
	Image analysis
	Machine learning
DC.description	Matsumoto Ryo, Kunisawa Shusuke, Nakamura Yusaku, et al. Image-Based Machine Learning of In Situ Captured Images of Die-Workpiece Interface in Forging. Defect and Diffusion Forum 441, 39 (2025); https://doi.org/10.4028/p-ex7ohs.
DCTERMS.abstract	The lubricant thickness in cold forging was estimated by machine learning of the in situ captured images of the die–workpiece contact interface. The images were in situ captured by a high-speed camera from the backside of the transparent glass die during forging of commercially pure aluminum workpiece. On the other hand, the images of the lubricated workpiece were individually captured as training images for random forest with classification. The classification accuracy of the lubricant thickness was confirmed to be approximately 75% (classification ability: 5–10 μm in lubricant thickness) in the training images with 22,500 px (50 px/mm). The in situ captured images of the die–workpiece contact interface during forging were classified by random forest using the training images. The estimated lubricant thickness of the in situ captured image almost agreed with the lubricant thickness estimated from the mean brightness value of the in situ captured image.
DC.format	100688
citation_title	Image-Based Machine Learning of In Situ Captured Images of Die-Workpiece Interface in Forging
citation_author	Matsumoto, Ryo
	Kunisawa, Shusuke
	Nakamura, Yusaku
	Utsunomiya, Hiroshi
citation_publisher	Trans Tech Publications, Ltd.
citation_language	英語
citation_date	2025-03-26
citation_journal_title	Defect and Diffusion Forum
citation_volume	441
citation_issue	9
citation_firstpage	39
citation_lastpage	43
citation_doi	https://doi.org/10.4028/p-ex7ohs
citation_public_url	https://hdl.handle.net/11094/100688

Image-Based Machine Learning of In Situ Captured Images of Die-Workpiece Interface in Forging

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