Convolutional Graph Thermography for Subsurface Defect Detection in Polymer Composites

Infrared thermography for quality assessment of polymer composites has gained increasing attention with the development of various thermographic data analysis methods. However, low-quality thermal images containing noise and inhomogeneous backgrounds restrict the defect detection performance of thes...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 71; S. 1
Hauptverfasser:	Liu, Kaixin, Yu, Qing, Liu, Yi, Yang, Jianguo, Yao, Yuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Background noise Carbon fiber reinforced plastics Convolution convolutional autoencoder Data analysis Decoding defect detection Defects Feature extraction Fiber reinforced polymers Image quality Infrared imaging infrared thermography manifold learning noise reduction Non-destructive testing Nonhomogeneous media Polymer matrix composites Polymers Quality assessment Temperature measurement Thermal imaging Thermography Training
ISSN:	0018-9456, 1557-9662
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Abstract	Infrared thermography for quality assessment of polymer composites has gained increasing attention with the development of various thermographic data analysis methods. However, low-quality thermal images containing noise and inhomogeneous backgrounds restrict the defect detection performance of these methods. In this work, a convolutional graph thermography (CGT) method is proposed for subsurface defect detection in polymer composites. With a developed convolutional autoencoder thermogram enhancer, the noise and inhomogeneous background of the thermal image can be reduced. Subsequently, a graph-based dimensionality reduction method performs feature extraction on the enhanced thermograms. Consequently, the low-dimensional intrinsic manifolds of the high-dimensional thermographic data are characterized to highlight the defects visually. Two case studies on carbon fiber reinforced polymers demonstrated the performance of the proposed method.
AbstractList	Infrared thermography for quality assessment of polymer composites has gained increasing attention with the development of various thermographic data analysis methods. However, low-quality thermal images containing noise and inhomogeneous backgrounds restrict the defect detection performance of these methods. In this work, a convolutional graph thermography (CGT) method is proposed for subsurface defect detection in polymer composites. With a developed convolutional autoencoder thermogram enhancer, the noise and inhomogeneous background of the thermal image can be reduced. Subsequently, a graph-based dimensionality reduction method performs feature extraction on the enhanced thermograms. Consequently, the low-dimensional intrinsic manifolds of the high-dimensional thermographic data are characterized to highlight the defects visually. Two case studies on carbon fiber reinforced polymers demonstrated the performance of the proposed method.
Author	Yu, Qing Yang, Jianguo Liu, Kaixin Yao, Yuan Liu, Yi
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SubjectTerms	Background noise Carbon fiber reinforced plastics Convolution convolutional autoencoder Data analysis Decoding defect detection Defects Feature extraction Fiber reinforced polymers Image quality Infrared imaging infrared thermography manifold learning noise reduction Non-destructive testing Nonhomogeneous media Polymer matrix composites Polymers Quality assessment Temperature measurement Thermal imaging Thermography Training
Title	Convolutional Graph Thermography for Subsurface Defect Detection in Polymer Composites
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