Journal
NDT & E INTERNATIONAL
Volume 125, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ndteint.2021.102561
Keywords
Infrared thermography; Hybrid aluminium-CFRP; Radiance calibration; Geometric correction; Probabilistic de-noising
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In this study, infrared thermography is used to detect defects on a 3D hybrid aluminium-CFRP composite structure. A new unsupervised probabilistic low-rank component factorization thermographic denoising model is proposed to improve image performance and defect visualization. Signal profiles and standard deviation analysis are used to assess the results and compare with x-ray CT inspections.
In this work, infrared thermography is used to detect defects on a 3D hybrid aluminium-CFRP composite structure. First, radiometric calibration and geometric distortion correction are performed for 3D inspection. Second, we propose a new unsupervised probabilistic low-rank component factorization thermographic denoising model to improve image performance and defect visualization. Signal profiles and standard deviation analysis is used to assess the results, and x-ray CT inspections are compared to the infrared inspection results. Finally, we can conclude that the proposed algorithm can detect voids and resin rich areas presenting a better image performance if compared to direct infrared inspection results.
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