Deep-Learning-Assisted Resonant Ultrasound Spectroscopy for Cubic Solids

Fukuda, Hiroki; Nagakubo, Akira; Wright, Oliver B.; Kyotani, Kazuhiro; Ogi, Hirotsugu

doi:https://doi.org/10.1103/physrevapplied.20.034048

Deep-Learning-Assisted Resonant Ultrasound Spectroscopy for Cubic Solids

Fukuda, Hiroki; Nagakubo, Akira; Wright, Oliver B. et al.

Physical Review Applied, 2023, 20, 034048

アクセス数:488件（2025-08-23 15:59 集計）

固定URL: https://hdl.handle.net/11094/92584

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タイトル	Deep-Learning-Assisted Resonant Ultrasound Spectroscopy for Cubic Solids
著者	著者名 Fukuda, Hiroki
	NRID 1000070751113 1000070751113 ORCID 0000-0001-7494-5099 0000-0001-7494-5099 researchmap permalink nagakubo nagakubo Scopus Author ID 55329400300 55329400300 著者名 Nagakubo, Akira
	著者名 Wright, Oliver B.
	著者名 Kyotani, Kazuhiro
	NRID 1000090252626 1000090252626 researchmap permalink read0042771 read0042771 Scopus Author ID 35563683500 35563683500 著者名 Ogi, Hirotsugu
内容	Fukuda Hiroki, Nagakubo Akira, Wright Oliver B., et al. Deep-Learning-Assisted Resonant Ultrasound Spectroscopy for Cubic Solids. Physical Review Applied 20, 228 (2023); https://doi.org/10.1103/physrevapplied.20.034048.
抄録	This paper solves a long-standing problem in resonant ultrasound spectroscopy (RUS) for cubic solids through the use of two-dimensional deep learning (DL). By means of inverse methods, conventional RUS can determine all independent elastic constants of a crystalline solid specimen by comparing measured and calculated free-vibration resonance frequencies. However, to avoid invalid local minima in the inverse process, good initial guesses of the elastic constants must be available. Here, we propose a DL scheme to remedy this problem, which utilizes an input elasticity image composed of three layers obtained from resonance frequency data. After network training, this scheme is executed in two steps: DL processing by a neural network to output elastic constants in a Blackman diagram classification, followed by the use of a regression network around the classified point in this diagram for more accurate determination. By means of simulations, we demonstrate that this DL scheme yields the principal elastic constants within an error of approximately 5% without any inverse processing even in the unfavorable case of five missing modes for 111 existing cubic crystals.
出版者	American Physical Society (APS)
収録物名	Physical Review Applied
巻(号)	20
ページ	034048
刊行年月	2023-09-21
言語	英語
ハンドルURL	https://hdl.handle.net/11094/92584
eISSN	23317019
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1103/physrevapplied.20.034048
アクセス権	open access
権利情報	Copyright 2023 by the American Physical Society
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	Physical Review Applied. 2023, 20, p. 034048
DC.title	Deep-Learning-Assisted Resonant Ultrasound Spectroscopy for Cubic Solids
DC.creator	Fukuda, Hiroki
	Nagakubo, Akira
	Wright, Oliver B.
	Kyotani, Kazuhiro
	Ogi, Hirotsugu
DC.publisher	American Physical Society (APS)
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2023-09-21
DC.identifier	https://doi.org/10.1103/physrevapplied.20.034048
DC.description	Fukuda Hiroki, Nagakubo Akira, Wright Oliver B., et al. Deep-Learning-Assisted Resonant Ultrasound Spectroscopy for Cubic Solids. Physical Review Applied 20, 228 (2023); https://doi.org/10.1103/physrevapplied.20.034048.
DCTERMS.abstract	This paper solves a long-standing problem in resonant ultrasound spectroscopy (RUS) for cubic solids through the use of two-dimensional deep learning (DL). By means of inverse methods, conventional RUS can determine all independent elastic constants of a crystalline solid specimen by comparing measured and calculated free-vibration resonance frequencies. However, to avoid invalid local minima in the inverse process, good initial guesses of the elastic constants must be available. Here, we propose a DL scheme to remedy this problem, which utilizes an input elasticity image composed of three layers obtained from resonance frequency data. After network training, this scheme is executed in two steps: DL processing by a neural network to output elastic constants in a Blackman diagram classification, followed by the use of a regression network around the classified point in this diagram for more accurate determination. By means of simulations, we demonstrate that this DL scheme yields the principal elastic constants within an error of approximately 5% without any inverse processing even in the unfavorable case of five missing modes for 111 existing cubic crystals.
DC.rights	Copyright 2023 by the American Physical Society
DC.format	92584
citation_title	Deep-Learning-Assisted Resonant Ultrasound Spectroscopy for Cubic Solids
citation_author	Fukuda, Hiroki
	Nagakubo, Akira
	Wright, Oliver B.
	Kyotani, Kazuhiro
	Ogi, Hirotsugu
citation_publisher	American Physical Society (APS)
citation_language	英語
citation_date	2023-09-21
citation_journal_title	Physical Review Applied
citation_volume	20
citation_firstpage	034048
citation_doi	https://doi.org/10.1103/physrevapplied.20.034048
citation_public_url	https://hdl.handle.net/11094/92584

Deep-Learning-Assisted Resonant Ultrasound Spectroscopy for Cubic Solids

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