Automated Damage Detection of (C/C)/Si/SiC Composite Using Vibration Modes with Deep Neural Networks

Discontinuous carbon fiber-carbon matrix composites dispersed Si/SiC matrix composites have complicated microstructures that consist of four phases (C/C, Si, SiC, and C/SiC). The crack stability significantly depends on their geometrical arrangement. Nondestructive evaluation is needed to maintain t...

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Autores principales:	Chihiro Shibata, Naohiro Shichijo, Johei Matsuoka, Yuriko Takeshima, Jenn-Ming Yang, Yoshihisa Tanaka, Yutaka Kagawa
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	nondestructive evaluation vibration and resonance anomaly detection deep learning convolutional neural networks auto-encoders Technology T Science Q
Acceso en línea:	https://doaj.org/article/6d0d1ed25eaa4f6abfbfe82bcd36d49f
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spelling	oai:doaj.org-article:6d0d1ed25eaa4f6abfbfe82bcd36d49f2021-11-25T18:03:17ZAutomated Damage Detection of (C/C)/Si/SiC Composite Using Vibration Modes with Deep Neural Networks10.3390/jcs51103012504-477Xhttps://doaj.org/article/6d0d1ed25eaa4f6abfbfe82bcd36d49f2021-11-01T00:00:00Zhttps://www.mdpi.com/2504-477X/5/11/301https://doaj.org/toc/2504-477XDiscontinuous carbon fiber-carbon matrix composites dispersed Si/SiC matrix composites have complicated microstructures that consist of four phases (C/C, Si, SiC, and C/SiC). The crack stability significantly depends on their geometrical arrangement. Nondestructive evaluation is needed to maintain the components in their safe condition. Although several nondestructive evaluation methods such as the Eddy current have been developed, any set of them is still inadequate in order to cover all of the scales and aspects that (C/C)/Si/SiC composites comprise. We propose a new method for nondestructive evaluation using vibration/resonance modes and deep learning. The assumed resolution is mm-order (approx. 1–10 mm), which laser vibrometers are generally capable of handling sufficiently. We utilize deep neural networks called convolutional auto-encoders for inferring damaged areas from vibration modes, which is a so-called inverse problem and infeasible to solve numerically in most cases. We solve this inference problem by training convolutional auto-encoders using vibration modes obtained from a non-damaged specimen with various frequencies as the dataset. Experimental results show that the proposed method successfully detects the damaged areas of validation specimens. One of the noteworthy points of this method is that we need only a few specimens for training deep neural networks, which generally require a large amount of data.Chihiro ShibataNaohiro ShichijoJohei MatsuokaYuriko TakeshimaJenn-Ming YangYoshihisa TanakaYutaka KagawaMDPI AGarticlenondestructive evaluationvibration and resonanceanomaly detectiondeep learningconvolutional neural networksauto-encodersTechnologyTScienceQENJournal of Composites Science, Vol 5, Iss 301, p 301 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	nondestructive evaluation vibration and resonance anomaly detection deep learning convolutional neural networks auto-encoders Technology T Science Q
spellingShingle	nondestructive evaluation vibration and resonance anomaly detection deep learning convolutional neural networks auto-encoders Technology T Science Q Chihiro Shibata Naohiro Shichijo Johei Matsuoka Yuriko Takeshima Jenn-Ming Yang Yoshihisa Tanaka Yutaka Kagawa Automated Damage Detection of (C/C)/Si/SiC Composite Using Vibration Modes with Deep Neural Networks
description	Discontinuous carbon fiber-carbon matrix composites dispersed Si/SiC matrix composites have complicated microstructures that consist of four phases (C/C, Si, SiC, and C/SiC). The crack stability significantly depends on their geometrical arrangement. Nondestructive evaluation is needed to maintain the components in their safe condition. Although several nondestructive evaluation methods such as the Eddy current have been developed, any set of them is still inadequate in order to cover all of the scales and aspects that (C/C)/Si/SiC composites comprise. We propose a new method for nondestructive evaluation using vibration/resonance modes and deep learning. The assumed resolution is mm-order (approx. 1–10 mm), which laser vibrometers are generally capable of handling sufficiently. We utilize deep neural networks called convolutional auto-encoders for inferring damaged areas from vibration modes, which is a so-called inverse problem and infeasible to solve numerically in most cases. We solve this inference problem by training convolutional auto-encoders using vibration modes obtained from a non-damaged specimen with various frequencies as the dataset. Experimental results show that the proposed method successfully detects the damaged areas of validation specimens. One of the noteworthy points of this method is that we need only a few specimens for training deep neural networks, which generally require a large amount of data.
format	article
author	Chihiro Shibata Naohiro Shichijo Johei Matsuoka Yuriko Takeshima Jenn-Ming Yang Yoshihisa Tanaka Yutaka Kagawa
author_facet	Chihiro Shibata Naohiro Shichijo Johei Matsuoka Yuriko Takeshima Jenn-Ming Yang Yoshihisa Tanaka Yutaka Kagawa
author_sort	Chihiro Shibata
title	Automated Damage Detection of (C/C)/Si/SiC Composite Using Vibration Modes with Deep Neural Networks
title_short	Automated Damage Detection of (C/C)/Si/SiC Composite Using Vibration Modes with Deep Neural Networks
title_full	Automated Damage Detection of (C/C)/Si/SiC Composite Using Vibration Modes with Deep Neural Networks
title_fullStr	Automated Damage Detection of (C/C)/Si/SiC Composite Using Vibration Modes with Deep Neural Networks
title_full_unstemmed	Automated Damage Detection of (C/C)/Si/SiC Composite Using Vibration Modes with Deep Neural Networks
title_sort	automated damage detection of (c/c)/si/sic composite using vibration modes with deep neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6d0d1ed25eaa4f6abfbfe82bcd36d49f
work_keys_str_mv	AT chihiroshibata automateddamagedetectionofccsisiccompositeusingvibrationmodeswithdeepneuralnetworks AT naohiroshichijo automateddamagedetectionofccsisiccompositeusingvibrationmodeswithdeepneuralnetworks AT joheimatsuoka automateddamagedetectionofccsisiccompositeusingvibrationmodeswithdeepneuralnetworks AT yurikotakeshima automateddamagedetectionofccsisiccompositeusingvibrationmodeswithdeepneuralnetworks AT jennmingyang automateddamagedetectionofccsisiccompositeusingvibrationmodeswithdeepneuralnetworks AT yoshihisatanaka automateddamagedetectionofccsisiccompositeusingvibrationmodeswithdeepneuralnetworks AT yutakakagawa automateddamagedetectionofccsisiccompositeusingvibrationmodeswithdeepneuralnetworks
_version_	1718411684437032960

Automated Damage Detection of (C/C)/Si/SiC Composite Using Vibration Modes with Deep Neural Networks

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