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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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 71; p. 1
Main Authors: Liu, Kaixin, Yu, Qing, Liu, Yi, Yang, Jianguo, Yao, Yuan
Format: Journal Article
Language:English
Published: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2022.3205906