NLFM infrared thermal wave detection system was established to detect and
analyze debonding defects of different sizes of CFRP laminates. Principal
component analysis, thermal signal reconstruction and total harmonic
distortion are used to process the infrared image sequence collected by
thermal imager, and the processing effect is evaluated by signal-to-noise
ratio. Compared with the other two image sequence processing algorithms, PCA
has better effect, can effectively improve the signal-to-noise ratio, and
the defect edge in the processed image is clearer.
Aiming at the debonding defect of carbon fiber reinforced polymer laminates,
an infrared phase-locked thermal imaging inspection system was established,
and the influence of different defect diameter and depth parameters on the
test was analyzed. The principal component analysis algorithm and
Karhunen-Loeve Transform algorithm are used to process the image sequence,
and the signal-to-noise ratio is calculated. It is concluded that principal
component analysis algorithm can improve the image quality more. Gray
enhancement and sharpening filter are used to improve the image clarity,
thus accurately segmenting the defect features, and realize a clear and
intuitive visual image.
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