PHASED ARRAYS TECHNIQUES AND SPLIT SPECTRUM
PROCESSING FOR INSPECTION OF THICK TITANIUM CASTING
COMPONENTS
J. Banchet1, R. Sicard1'2, D.E. Zellouf2 and A. Chahbaz1
^D Tech, 505 Blvd. Du Pare Technologique, Quebec, QC, Canada, GIF 4S9
2
Institut de Recherche sur 1'Hydrogene, Universite du Quebec Trois-Rivieres, CP500,
Trois-Rivieres, QC, Canada, G9A 5H7
ABSTRACT. In aircraft structures, titanium parts and engine members are critical structural components,
and their inspection crucial. However, these structures are very difficult to inspect ultrasonically because of
their large grain structure that increases noise drastically. In this work, phased array inspection setups were
developed to detected small defects such as simulated inclusions and porosity contained in thick titanium
casting blocks, which are frequently used in the aerospace industry. A Cut Spectrum Processing (CSP)-based
algorithm was then implemented on the acquired data by employing a set of parallel bandpass filters with
different center frequencies. This process led in substantial improvement of the signal to noise ratio and thus,
of detectability.
INTRODUCTION
It has been a number of years that discrimination between inclusions or defects and
structural noise has been an issue in the titanium casting industry. Through the later
process, large grains and their joints act as reflectors, whose ultrasonic echo can be
misinterpreted as coming from among others, a hard-alpha inclusion [1,2]. Although
evolution of the phase array technology brought improved capabilities of detectability and
rapidity to this type of inspection, it has not brought solutions to this discrimination
problem.
However, work has been done by various teams and researchers in order to solve this
issue. Modeling and several post-processing techniques were developed and tested to
respectively characterize this structural noise[3,4], and remove it without altering the
inclusion signal. One of these techniques is the Cut-Spectrum-Processing (CSP) [4]
Based on the Quasi-Frequency Diversity principle (QFD), the CSP algorithm has
been designed to remove as much as possible the non-coherent information embedded in a
signal. Thus, one of its capabilities lies in the elimination of the background noise
encountered during composite materials inspection. However, when inspecting granular
materials, echoes resulting from the diffraction on grains could be as frequency coherent as
the defect echo and the CSP algorithm could therefore not be able to discriminate between
them.
In this work, we apply a modified version of a CSP algorithm [4] to phased array
scans performed on Titanium casted samples containing Flat Bottom Holes (FBH) as a first
CP657, Review of Quantitative Nondestructive Evaluation Vol. 22, ed. by D. O. Thompson and D. E. Chimenti
© 2003 American Institute of Physics 0-7354-0117-9/03/S20.00
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step of defect mimicking. After describing the experimental setup including the sample
characteristics, modifications brought to the CSP algorithm will be presented and results of
its application on the acquired scans discussed.
THEORY
The cut-spectrum processing is one of the most robust quasi-frequency diversity
algorithms, designed to remove the structural noise originating within the inspected
material from the interferences between the reflections from heterogeneities. Known as an
alternative to the split-spectrum processing (SSP), another QFD-based algorithm, its main
advantage lies in no prior knowledge of the defect echo frequency bandwidth4.
CSP lies on the following principles: as the time of apparition of a target echo (the
defect, in our case) is frequency independent and the target echo signal is relatively wide
band, removing a narrow band sample from the Fourier transform of this signal should
have no effect relative to the time localization of the target. Thus, passing the recorded
signal through a bank of narrow stop band filters (expansion phase) and applying to each of
the obtained spectrum an inverse Fast Fourier Transform results in a collection of signals
that allows noise decorrelation through comparing the latter signals at the same moment to
identify the coherent peak (defect peak) and remove all the non-coherent information
(extraction phase).
Nevertheless, removing a frequency bin from the spectrum could, in the worst case,
affect slightly the amplitude of the defect echo, without affecting, however, the time at
which the echo appears in the signal. It means that a range of values in which the amplitude
of the target echo must evolve needs to be set.
Equations (1) and (2) respectively present the expansion and extraction phases of the
CSP processing [4]:
m
with
if
Ek(co) = Q.
^min (*i ) > ^max (*ii )
(1)
0 < # < 1
QN
otherwise.
where em/w(ti) and emax(ti) are respectively the minimum and the maximum of the expanded
signals at instant t/ and q an adjustable parameter.
EXPERIMENTAL SETUP
A 2 MHz linear probe with 32 piezoelectric elements located 1.4mm apart was
plugged into a Focus system from R/D Tech acting as a pulser receiver and responsible for
applying the time delays yielding focalization. Inspection was performed at 0 degree in
immersion by raster scanning with a 3 motorized axis (X,Y,Z) and 3 manual axis (rotation)
scanner mounted on an immersion tank.
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(b)
(a)
FIGURE 1. Step Block sample (a) and scanned FBHs (b).
The test sample (Figure 1) is composed of a titanium casted step block (steps of 1, 2,
3, 4 and 5 inches) with FBH #2, #3 and #5 located on each step. After the probe alignment
had been performed with respect to the sample surface, probe was set to yield a 25mm
water column. Raster scans were performed on the FBH presented in Table 1, with a
focalization depth corresponding to each FBH location.
RESULTS AND DISCUSSION
Figure 2 presents the C-scan obtained by raster scanning the above-mentioned FBH
prior and posterior to the CSP application. Although it is possible to identify the FBH from
the raw C-scans (figure 2 a-e), resolution and signal to noise ratio are strongly affected by
the surrounding grains. Applying the CSP algorithm to these signals provides background
noise removal and thus signal to noise ratio enhancement, but is however unable to
discriminate between grain and FBH reflections (figure 2 c,h and e,j). Modifications to the
CSP processing previously described need then to be developed.
FIGURE 2. FBH's C-scans prior (a-e) and posterior (f-j) to CSP processing. a,f FBH#2 at 1 inch, b,g
FBH#3 at 1 inch, c,h FBH#3 at 2 inches, d,i FBH#5 at 2 inches, e,j FBH#5 at 3 inches.
795
Flat-bottom r
Frequencf(samples)
100
150
Time (samples)
(b)
(a)
FIGURE 3. FBH #3 at Sinches A-scan (a) identifying grain and echo signal, and comparison of their
respective FFT (b).
The way an ultrasonic beam interacts with a FBH or a void differs from the way it
reflects on a grain. While a void is filled with air, a grain is a part of material that is only
separated from the surrounding material by a very thin interphase region. Whereas the
ultrasonic beam is going to be reflected on the material/air interface without any frequency
content variation, reflection on grain boundaries will lead to high frequency components
attenuation.
Figure 3 (a) presents an A -scan signal reflected from the FBH #3 at 2 inches depth.
After identify the echo coming from the FBH and one from a grain, respective spectra
resulting from an FFT transform of each echo are compared (b). Bandwidth of the defect
echo is obviously larger as higher frequencies are strongly attenuated in the grain echo
spectrum. This behavior was also observed on FBH #5 at 3 inches depth. Since the grain
echo spectrum fits the A-scan FFT, the CSP algorithm was not able to remove the grain
echo because the expansion has been performed within the grain echo bandwidth.
Thus, strong improvements of the CSP performances could be expected if we apply a
high pass filter prior to the expansion process. In order to preserve as much FBH
information as possible and discard as much grain information as possible, this filter should
remove all frequencies lower than the maximum of the A-scan spectrum.
Effect of such a high pass filter applied on FBH #5 at 3 inches can be observed on
Figure 4. It can be shown that FBH/grain amplitude ratio gains 7 dB, passing from 1.4 to
3.1.
Time (samples)
(a)
(b)
FIGURE 4. FBH#5 at 3inches A-scan prior (a) and posterior (b) to the described high pass filter.
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(o)
0)
FIGURE 5. Comparison between raw (a-e), high pass filtered (f-j) and high passed filtered and CSP
processed (k-o) FBH's C-scans.. a,f,k FBH#2 at 1 inch, b,g,l FBH#3 at 1 inch, c,h,m FBH#3 at 2 inches,
d,i,n FBH#5 at 2 inches, e,j,o FBH#5 at 3 inches.
It can be seen on Figure 5 that high-pass filtering improves FBH's definition or
resolution but still remains unable to remove entirely the grain acoustical signature (f-j).
However, besides SNR enhancement, grain/FBH discrimination becomes effective when
we perform the CSP algorithm on the A-scans already processed through the high pass
filter (k-o). This process is particularly effective on FBH#3 at 2 inches and FBH#5 at 2
inches cases versus a sole CSP processing, as comparison between Figure 2 and Figure 5
can show.
CONCLUSION
Through this study, a Titanium block containing Flat Bottom Holes located at
different depths was scanned with a Phased Array technique. In order to countervene the
lack of discrimination between grain information and FBH information, a Cut Spectrum
Processing was performed on the obtained scans. Although efficient in increasing the scans
SNR, CSP processing did not solve the discrimination issue. It was shown that application
of a high pass filter whose cut-off frequency is the maximum of the Scanned area A-scan's
FFT, followed by a CSP processing not only increased SNR but also was able to
discrimination between grain and FBH.
Although this technique seems promising, attention should be paid to the fact that real
defects such as hard-alpha inclusions do not have the same acoustical properties than FBH
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and are in this respect much closer to grain properties. High pass filtering may not be as
effective on these types of defects than on FBH.
REFERENCES
1. Papadakis, E. P., "Attenuation caused by scattering in polycrystalline media," in
Physical Acoustics, Masson, Paris, 1967, Vol4, pp. 269-327.
2. O'Donnell M., Jaynes E.T., Miller J.G., J. Acoust. Soc.Am. 69, 696-705 (1981).
3. Stepinski T., Ericsson L., Eriksson B. and Gustafsson M., "Quasi Frequency Diversity
Processing of Ultrasonic Signals - A Review", in Advances in Signal Processing for
Non-Destructive Evaluation of Materials, edited by X.P.V. Maldague, Kluwer
Academic Publ., 1994, pp. 49-58.
4. Ericsson L, Stepinski T., NDT&E International 25 (2), 59-64, (1992).
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