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Erschienen in:
Numerical Investigations on the Influence of Prestress on Lamb Wave Propagation Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

Site-Specific Defect Detection in Composite Using Solitary Waves Based on Deep Learning

verfasst von : Tae-Yeon Kim, Sangyoung Yoon, Chan Yeob Yeun, Wesley J. Cantwell, Chung-Suk Cho

Erschienen in: European Workshop on Structural Health Monitoring

Verlag: Springer International Publishing

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Abstract

We propose a real-time non-destructive evaluation technique for defect detection in composites using highly nonlinear solitary waves (HNSWs) and a deep learning algorithm based on the convolution neural network (CNN). This technique implements deep learning to identify the presence of defects and classify the defect locations in the thickness direction of composites through HNSWs with strong energy intensity and non-distortive nature. To collect HNSW datasets for training and validation of the deep learning algorithm, AS4/PEEK composite specimens with artificial delamination are fabricated and HNSW datasets are generated from the experimental setup of a granular crystal sensor. Testing pretrained CNN based algorithms verifies the performance of detecting and classifying defects by location in composite plates.

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Vorheriges Kapitel Supervised Deep Learning Algorithms for Delaminations Detection on Composites Panels by Wave Propagation Signals Analysis
Nächstes Kapitel Machine Learning Based Classification of Guided Wave Signals in the Context of Inter-specimen Variabilities
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Metadaten
Titel
Site-Specific Defect Detection in Composite Using Solitary Waves Based on Deep Learning
verfasst von
Tae-Yeon Kim
Sangyoung Yoon
Chan Yeob Yeun
Wesley J. Cantwell
Chung-Suk Cho
Copyright-Jahr
2023
Buch
European Workshop on Structural Health Monitoring

Print ISBN: 978-3-031-07321-2

Electronic ISBN: 978-3-031-07322-9

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-3-031-07322-9_45