Abstract:
Thermal non-destructive testing (TNDT) is one of
the emerging inspection and evaluation techniques mostly used
for subsurface defect detection in various industrial components.
Besides the conventional thermography techniques (such as
lock-in and pulse), recently introduced non-stationary thermal
wave imaging (NSTWI) techniques gained its applicability in
TNDT community due to their inherent testing capabilities such
as improved sensitivity and enhanced resolution in inspecting
and evaluating various solid materials for detecting subsurface
defects. Barker-coded thermal wave imaging (BCTWI) is a one
of the widely used NSTWI techniques, which facilitates the use
of low peak power heat sources in moderate experimentation
time in contrast to conventional TNDT techniques. In this paper,
the pulse compression favorable NSTWI (BCTWI), the reconstructed pulsed (main lobe) data have been considered and
processed using independent component analysis and named
Barker-coded independent component thermography (BCICT).
This proposed BCICT is implemented on a mild-steel sample to
detect the artificially simulated flat bottom circular holes located
at different depths inside it. The proposed technique extracts
the sub-surface details such as flaws/defects hidden inside the
sample by an unsupervised learning process, which helps in
eliminating the manual interpretation of subsurface defects. The
applicability of the proposed algorithm has been evaluated and
validated experimentally with two different excitations schemes
by considering the contrast and signal-to-noise ratio (SNR) as
figure of merit. The results indicate that the BCICT technique
offers higher contrast and SNR in comparison to conventional
pulse-based TNDT technique.
