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

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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.

Original languageBritish English
Title of host publicationEuropean Workshop on Structural Health Monitoring, EWSHM 2022, Volume 3
EditorsPiervincenzo Rizzo, Alberto Milazzo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages442-451
Number of pages10
ISBN (Print)9783031073212
DOIs
StatePublished - 2023
Event10th European Workshop on Structural Health Monitoring, EWSHM 2022 - Palermo, Italy
Duration: 4 Jul 20227 Jul 2022

Publication series

NameLecture Notes in Civil Engineering
Volume270 LNCE
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference10th European Workshop on Structural Health Monitoring, EWSHM 2022
Country/TerritoryItaly
CityPalermo
Period4/07/227/07/22

Keywords

  • AS4/PEEK
  • Convolution neural network
  • Delamination
  • Granular crystal
  • Machine learning
  • Non-destructive evaluation

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