Abstract:
Active thermography is a widely used nondestructive testing and evaluation technique (NDT&E) for
evaluating the properties of materials without impairing its
future usefulness. In this work, a mild steel sample made
of artificial flat-bottom holes at varied depths, was examined with the emerging non-stationary thermal wave imaging (NSTWI) technique,i.e. frequency modulated thermal wave
imaging (FMTWI). The pulse compression favorable of NSTWI
technique is eminent for compressing the applied thermal
energy into a narrow-compressed pulseto enhance the depth
resolution and sensitivity. In this work, pulse compressed
thermographic data generated from FMTWI experimentation
is analyzed with the unsupervised learning approach independent component analysis (ICA) to test their mutual return in the detection of the deep defects in a mild steel sample
and this proposed technique was referred to as frequency modulated independent component thermography (FMICT).
In comparison, the effect of FMICT was contrasted with othermethodsi.e. pulse compression of time domain and ICA of
feature space by considering the signal-to-noise ratio (SNR) as a figure of merit. Furthermore, a probability of detection
(POD) analysis framework based on the minimum threshold SNR criteria for apparent visibility of the defects has been
presented to assess the probability of identifying defects at various depths using such approaches. The influence of the
SNR threshold value for the above strategies on the POD curves has also been presented.
