4.2 Thermal data processing
Thermal sequences provided by the IR camera, were processed according to
the procedure presented in [40].
A signal reconstruction algorithm based on the least squares method was
performed in order to extract pixel by pixel from the generic thermal
signal S , Eq.7, its components:
\(S\left(\frac{N}{f}\right)=S_{\text{mean}}(\frac{N}{f})+S1\sin\left(\omega\frac{N}{f}+\varphi\right)+S2\cos\left(2\omega\frac{N}{f}+\varphi_{d}\right)\)(7)
where the Smean is the term describing the mean
temperature while S1 and S2 \(\ \)are respectively the
first and second amplitude harmonics, \(\varphi\) and \(\varphi_{d}\)are phase shift of first and second harmonics respectively and the
system pulsation is \(\omega=2\pi f\).
In this research, the first amplitude harmonic signal representative of
the thermoelastic signal and the related map were considered for the
analysis according to the procedure of Fig.4. The first consideration on
matrixes in Fig. 4, obtained by frequency domain analysis, is thatSmean does
not provide local information [40] while S1allows for assessing more local information[39-40]. The algorithm involves the following
steps according to Fig. 4:
- the size reduction to the gage length of mapsS1N, in
order to refer thermoelastic signal to the area controlled by
extensometer, the output map is(S1_red)N.
The subscript N represents the number of cycles at which thermal
sequence was recorded.
- a 2D median filtering of (S1_red)N maps. Each
output pixel represents the median value in a 3-by-3 neighbourhood
around the corresponding pixel of the input image,(S1_filt)N.
- the extraction of the mean value, 98th and
2nd percentiles,(S1_mean)N,
(S1_98prc)N and
(S1_2prc)N respectively, to make a preliminary
analysis of the behaviour of thermoelastic signal.
- the normalization of each (S1_filt)N map by
initial value (number of cyclesN0),(S1_filt)N/(S1_filt)N0.
- the extraction of the mean value, 98th and
2nd percentiles,(S1_mean)N/(S1_mean)N0,
(S1_98prc)N/(S1_98prc)N0and
(S1_2prc)N/(S1_2prc)N0respectively. These latter represent the thermal metrics used for
evaluating the damage. The use of percentile instead of maximum/minimum
values allows to avoid outliers in the analysis.
In Fig. 5, the evolution of percentiles and mean values of thermoelastic
signal (S1_mean)N ,
(S1_98prc)N and
(S1_2prc)N are reported. Each test was named as
sample/50-60-65-70/% UTS. The index
(S1_98prc)N , Fig. 5a, presents a variable trend
(increasing/decreasing) demonstrating the complexity of damage
mechanisms and their effects on composites. Fig. 5b reports the mean
values (S1_mean)N which seem more affected by
the influence of maximum signal. The signal
(S1_2prc)N in Fig. 5c exhibits a decreasing
behaviour through the cycles that is reproducible for each test. This
latter parameter seems to be more robust to follow the stiffness
degradation than others as confirmed by the analysis of[37].
Results