Infrared thermographic inspection of 3D hybrid aluminium-CFRP composite using different spectral bands and new unsupervised probabilistic low-rank component factorization model

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.

Automated summary

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.