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Article

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

1
The Center for Ceramic Matrix Composites, Tokyo University of Technology, Tokyo 192-0982, Japan
2
Faculty of Science and Engineering, Hosei University, Tokyo 102-8160, Japan
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School of Business Administration, Hitotsubashi University, Tokyo 186-8601, Japan
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School of Computer Science, Tokyo University of Technology, Tokyo 192-0982, Japan
5
School of Media Science, Tokyo University of Technology, Tokyo 192-0982, Japan
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Department of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: Gérard L. Vignoles
J. Compos. Sci. 2021, 5(11), 301; https://doi.org/10.3390/jcs5110301
Received: 23 September 2021 / Revised: 3 November 2021 / Accepted: 9 November 2021 / Published: 16 November 2021
(This article belongs to the Special Issue Ceramic-Matrix Composites)
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. View Full-Text
Keywords: nondestructive evaluation; vibration and resonance; anomaly detection; deep learning; convolutional neural networks; auto-encoders nondestructive evaluation; vibration and resonance; anomaly detection; deep learning; convolutional neural networks; auto-encoders
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MDPI and ACS Style

Shibata, C.; Shichijo, N.; Matsuoka, J.; Takeshima, Y.; Yang, J.-M.; Tanaka, Y.; Kagawa, Y. Automated Damage Detection of (C/C)/Si/SiC Composite Using Vibration Modes with Deep Neural Networks. J. Compos. Sci. 2021, 5, 301. https://doi.org/10.3390/jcs5110301

AMA Style

Shibata C, Shichijo N, Matsuoka J, Takeshima Y, Yang J-M, Tanaka Y, Kagawa Y. Automated Damage Detection of (C/C)/Si/SiC Composite Using Vibration Modes with Deep Neural Networks. Journal of Composites Science. 2021; 5(11):301. https://doi.org/10.3390/jcs5110301

Chicago/Turabian Style

Shibata, Chihiro, Naohiro Shichijo, Johei Matsuoka, Yuriko Takeshima, Jenn-Ming Yang, Yoshihisa Tanaka, and Yutaka Kagawa. 2021. "Automated Damage Detection of (C/C)/Si/SiC Composite Using Vibration Modes with Deep Neural Networks" Journal of Composites Science 5, no. 11: 301. https://doi.org/10.3390/jcs5110301

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