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Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography

Increasing machine learning methods are being applied to infrared non-destructive assessment for internal defects assessment of composite materials. However, most of them extract only linear features, which is not in accord with the nonlinear characteristics of infrared data. Moreover, limited infra...

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Detalles Bibliográficos
Autores principales: Liu, Kaixin, Ma, Zhengyang, Liu, Yi, Yang, Jianguo, Yao, Yuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962653/
https://www.ncbi.nlm.nih.gov/pubmed/33800303
http://dx.doi.org/10.3390/polym13050825
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author Liu, Kaixin
Ma, Zhengyang
Liu, Yi
Yang, Jianguo
Yao, Yuan
author_facet Liu, Kaixin
Ma, Zhengyang
Liu, Yi
Yang, Jianguo
Yao, Yuan
author_sort Liu, Kaixin
collection PubMed
description Increasing machine learning methods are being applied to infrared non-destructive assessment for internal defects assessment of composite materials. However, most of them extract only linear features, which is not in accord with the nonlinear characteristics of infrared data. Moreover, limited infrared images tend to restrict the data analysis capabilities of machine learning methods. In this work, a novel generative kernel principal component thermography (GKPCT) method is proposed for defect detection of carbon fiber reinforced polymer (CFRP) composites. Specifically, the spectral normalization generative adversarial network is proposed to augment the thermograms for model construction. Sequentially, the KPCT method is used by feature mapping of all thermogram data using kernel principal component analysis, which allows for differentiation of defects and background in the dimensionality-reduced data. Additionally, a defect-background separation metric is designed to help the performance evaluation of data analysis methods. Experimental results on CFRP demonstrate the feasibility and advantages of the proposed GKPCT method.
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spelling pubmed-79626532021-03-17 Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography Liu, Kaixin Ma, Zhengyang Liu, Yi Yang, Jianguo Yao, Yuan Polymers (Basel) Article Increasing machine learning methods are being applied to infrared non-destructive assessment for internal defects assessment of composite materials. However, most of them extract only linear features, which is not in accord with the nonlinear characteristics of infrared data. Moreover, limited infrared images tend to restrict the data analysis capabilities of machine learning methods. In this work, a novel generative kernel principal component thermography (GKPCT) method is proposed for defect detection of carbon fiber reinforced polymer (CFRP) composites. Specifically, the spectral normalization generative adversarial network is proposed to augment the thermograms for model construction. Sequentially, the KPCT method is used by feature mapping of all thermogram data using kernel principal component analysis, which allows for differentiation of defects and background in the dimensionality-reduced data. Additionally, a defect-background separation metric is designed to help the performance evaluation of data analysis methods. Experimental results on CFRP demonstrate the feasibility and advantages of the proposed GKPCT method. MDPI 2021-03-08 /pmc/articles/PMC7962653/ /pubmed/33800303 http://dx.doi.org/10.3390/polym13050825 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Kaixin
Ma, Zhengyang
Liu, Yi
Yang, Jianguo
Yao, Yuan
Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography
title Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography
title_full Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography
title_fullStr Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography
title_full_unstemmed Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography
title_short Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography
title_sort enhanced defect detection in carbon fiber reinforced polymer composites via generative kernel principal component thermography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962653/
https://www.ncbi.nlm.nih.gov/pubmed/33800303
http://dx.doi.org/10.3390/polym13050825
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