ARTIFICIAL NEURAL NETWORK BASED ALGORITHM FOR
ACOUSTIC IMPACT BASED NONDESTRUCTIVE
PROCESS MONITORING OF COMPOSITE PRODUCTS
V Srivatsan, Krishnan Balasubramaniam* and N. V. Nair
Center for Nondestructive Evaluation,
^Department of Mechanical Engineering,
Indian Institute of Technology, Chennai-600036, INDIA
ABSTRACT. Damages like cracks, delaminations, etc., in composite parts have traditionally been
evaluated using manual methods like acoustic impact (using measurements in the audio frequencies).
This technique is currently used during manufacturing for product quality testing and later for
maintenance and assurance of structural integrity. The automation of this technique will
significantly improve the reliability of inspection. The signals obtained from the composites are
analyzed using signal-processing techniques in the time-frequency domain to build a robust
algorithm for detection and identification of defects. A feature vector is constructed using these
techniques and then applied to a neural network for defect identification. Comparative studies are
conducted to search for the best and most comprehensive feature vector. Results using different
signal processing techniques are presented. Similarly comparative results are presented between two
different kinds of neural networks (namely Radial Basis functions and MLP) and various
architectures in each kind. A low cost data acquisition system has also been developed for acquiring
audio signals using the sound card and the microphone in a multi-media PC.
INTRODUCTION
Damage like cracks, voids and de-laminations in composite parts that are
manufactured in high volumes must be detected during manufacture to ensure quality.
This process requires the skill and experience of an operator, and thus, the reliability of the
system is not certain. The concept of acoustic impact itself is also longstanding, having
come to light through the potters of ancient ages and more rigorously automated in recent
times for NDE of composites and aerospace components for maintenance applications. [19] In this paper, we have attempted to develop an automated process based on the similar
principles that can be directly applied to quality assurance of composite parts in a mass
production environment. The specimens are excited by an impact actuated by a solenoid,
and the acoustic signal is recorded using a microphone and a multimedia PC. Frequency
extraction is done on the time domain signal and the frequency spectral data was used as
input to a neural network to detects the defects or delaminations in the composites at very
high production rates (2-5 parts per second).
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/$20.00
1651
PRODUCTION PROCESS
PRODUCTION PROCESS
Usually homogeneous composites are fabricated by compressing the powdered
Usually homogeneous composites are fabricated by compressing the powdered
constituents under high temperature and pressure. The composition and dimensions of the
constituents under high temperature and pressure. The composition and dimensions of the
lining is normally manipulated to achieve the right values of the control parameters. A
lining is normally manipulated to achieve the right values of the control parameters. A
typical
example is brake linings in which case the usual control parameters are coefficient
typical example is brake linings in which case the usual control parameters are coefficient
ofoffriction
and thermal conductivity. During the compression process, by product gases
friction and thermal conductivity. During the compression process, by product gases
are
formed,
these
are formed,and
andproper
properarrangements
arrangements need
need to
to be
be made
made to
to vent
vent them.
them. Occasionally,
Occasionally, these
gases
do
not
find
an
escape
route
and
thus
form
voids
within
the
composite
part
causing
gases do not find an escape route and thus form voids within the composite part causing
porosity
porosityoror'blisters'.
‘blisters’.
These
As the
the
Thesevoids
voidsand
and 'blisters'
‘blisters’ adversely
adversely affect
affect the
the performance
performance of
of the
the parts.
parts. As
part
wears
out
due
to
repeated
use,
the
voids
come
to
the
surface.
This
has
a
heavily
part wears out due to repeated use, the voids come to the surface. This has a heavily
detrimental
detect and
and
detrimentaleffect
effecton
onthe
theperformance
performance of
of the
the part.
part. This
This makes
makes itit essential
essential to
to detect
reject
defective
parts
on
the
manufacturing
line
itself.
Unless
the
voids
are
of
significant
reject defective parts on the manufacturing line itself. Unless the voids are of significant
size
be
size(this
(thisresults
resultsininaaconspicuous
conspicuousbulge
bulgeon
onthe
the surface
surface of
of the
the part),
part), they
they cannot
cannot usually
usually be
detected
by
the
naked
eye.
detected by the naked eye.
ACOUSTIC
ACOUSTICIMPACT
IMPACTBASED
BASEDRESONANCE
RESONANCE
On
Onimpact,
impact,any
anysolid
solidwould
wouldemit
emitsound
sound due
duetotoresonance
resonance in
in either
either of
of the
the following
following
two
twomanners.
manners.The
The first
first being
being the
the actual
actual resonance
resonance of
of the
the structure
structure and
and the
the other
other the
the
formation
of
standing
waves
inside
the
solid.
One
of
these
frequencies
usually
lies
in the
formation of standing waves inside the solid. One of these frequencies
audible
audiblerange
rangeofoffrequencies.
frequencies.The
Thepresence
presence of
of aa defect
defect such
such as
as aa delamination
delamination causes the
object
objecttotoresonate
resonatewith
withaadifferent
differentfrequency
frequencythan
than aa 'perfect'
‘perfect’ or
or defect
defect less
less composite. This
isisthe
resonance
thebasic
basicprinciple
principleon
onwhich
whichour
ourdetection
detection technique
technique isis based.
based. The
The modes of resonance
are
aredepicted
depictedininthe
theFig.
Fig.11below.
below.
FEM
were
FEMbased
basedmodels
modelsof
of aatypical
typical composite
composite part
part of
of rectangular
rectangular cross-section were
studied
studiedusing
usingcommercially
commerciallyavailable
available software
software so
so as
as to
to obtain
obtain an
an initial
initial insight into the
resonance
study are
are
resonancemodes
modes available.
available. The
The mode
mode shapes
shapes obtained
obtained as
as aa result
result of this study
presented
presentedbelow.
below.Fig
Fig22depicts
depictsthe
thefirst
firstfour
fourmode
modeshapes
shapes
liiiiiiiiiiipi^^
:
fiii it: 4»i:
FIGURE
FIGURE1.1.Resonant
Resonantmodes.
modes.
FIGURE2.2.FEM
FEMprediction
predictionofofmodes
modesofofresonance.
resonance.
FIGURE
1652
INITIAL EXPERIMENTAL SETUP
INITIAL EXPERIMENTAL SETUP
Impactor
Impactor
The design of the impact mechanism becomes a critical issue since the interference
Thedue
design
of the impact
becomes is
a critical
issue since the
interferencethe
occurring
to acoustic
signalsmechanism
from the impactor
quite significant.
Standardizing
occurring
due
to
acoustic
signals
from
the
impactor
is
quite
significant.
Standardizing
the
impactor, though, can relatively easily circumvent this problem. We used a solenoid-based
impactor,
though,
can
relatively
easily
circumvent
this
problem.
We
used
a
solenoid-based
impactor with a ball hammerhead. This ball was introduced essentially to increase the
impactor
with especially
a ball hammerhead.
Thisimpactors
ball was had
introduced
essentially
increase
weight since
lightweight
a tendency
to liftto off
and the
cause
weight
since
especially
lightweight
impactors
had
a
tendency
to
lift
off
and
cause
secondary impacts. The solenoid has a rated frequency of 12 Hz.
secondary impacts. The solenoid has a rated frequency of 12 Hz.
Initial Test Stand
Initial Test Stand
The test stand for the experiment was designed using aluminum L sections and this
The
test stand for the experiment was designed using aluminum L sections and this
was
acoustically
insulated from
fromthe
thecomposite
compositepart
partusing
usingStyrofoam
Styrofoam
base.
The
height
was acoustically insulated
base.
The
height
of of
the
stand
was
also
chosen
so
as
to
remove
air
column
resonance.
Fig.
3
shows
the
the stand was also chosen so as to remove air column resonance. Fig. 3 shows the testtest
stand.
stand.
Data Acquisition
Acquisition
Data
The data
data acquisition
acquisitionmechanism
mechanismwas
waskept
keptasassimple
simpleasaspossible
possible
allow
The
to to
allow
forfor
thethe
easy adaptability
adaptability into
into aa production
productionenvironment.
environment.We
Wehave
haveshown
shown
that
a simple
passive
easy
that
a simple
passive
computer microphone
microphonecan
canbe
beused
usedtotodetect
detectthe
thedifferences
differencesin in
frequencies.Our
Our
prototype
computer
frequencies.
prototype
was
built
with
a
computer
microphone
and
a
LabView
based
data
acquisition
system
was built with a computer microphone and a LabView based data acquisition system forfor
determining the
the frequency
frequencyspectra.
spectra.The
TheANN
ANNanalysis
analysiswas
wascarried
carried
initially
post
determining
outout
initially
in in
thethe
post
processingmode.
mode.Fig.
Fig.44shows
showsaascreen
screenshot
shotofofthe
thedata
dataacquisition
acquisition
software.
processing
software.
Impactor
Impactor
Composite db
Composite
Styrofoam
Styrofoam
FIGURE
FIGURE3.3. Test
Testsetup.
setup.
FIGURE 4.
4. Screen
Screenshot
shotofofdata
dataacquisition
acquisitionsoftware.
software.
FIGURE
1653
ARTIFICIAL NEURAL NETWORKS
There is a distinct difference in the frequencies across different part thickness and
compositions. This difference cannot be effectively tacked with a simple rule based system
based simply on the frequencies of resonance. The complete and accurate knowledge of
the composite part's composition is almost impossible in real-life scenario making the
accurate prediction of the resonance frequencies very difficult. An ANN based approach
assumes no knowledge of the composition or thickness of the parts. Moreover an on-site
train and use approach is more attractive to a production plant, which would need to
conduct an in-depth study each time it changed its parameters, if it were to rely on rule
based methods.
Various architectures were tried out with two different types of classifiers, the
Multi Layer Perceptron (MLP) and the Radial Basis Function (RBF) based neural
networks. These were chosen since they are the two most commonly used classifiers and
also the fastest. They were trained with various source vectors, all in the frequency
domain.
RESULTS
The first set of training of the MLP was done with the complete frequency domain
data (comprising of 517 data points). This training set was later modified using a data set
with the 10 values of frequency appearing with the highest amplitude in the frequency
data. Then the data was collected at 9 points on each specimen and all the 90 points were
used for training. The training was successful and the time taken was found to be generally
proportional to the input vector size since the rest of the architecture was kept unchanged
and fully connected at all time. Testing gave best results with the 90-point data with less
than 10% error (1 missed call in 13 samples).
The RBF network was trained with the 10-point data and the 90-point data. The
time taken for training only doubled in the case of the 90-point data, showing promise that
this is less affected by the size of the input vector. The testing correlations were found to
be slightly better than the MLP case, (1 error in 15 data sets, correlation of 0.932). It must
also be said that though the use of the 517 point data is not time-efficient, the results of
testing using that input vector with the MLP network gave extremely promising results - 0
wrong calls among 50 data sets.
PROTOTYPE CONVEYOR INSPECTION SYSTEM
A prototype of the entire mechanism was designed which can be inserted into a
production environment. The conveyer is built using Aluminum L sections for a frame
with a cotton belt. A stepper motor given using an MS shaft operates the drive. A
computer microphone is used for data acquisition. The setup is designed to fit as the last
end of a conveyer belt. The figure of the entire system is shown in Fig 5 below.
Software was developed using the MLP ANN algorithm with lights indicating
GO/ NO-GO for each component. In Fig. 6, the screenshot of the software written in
Labview is illustrated.
1654
FIGURE 5. Complete system, Impact station (two views) and Drive system (in counter clockwise order).
FIGURE 5. Complete system, Impact station (two views) and Drive system (in counter clockwise order).
FIGURE 6.
6. Automated
Automated Software.
Software.
FIGURE
FUTURE WORK
WORK
FUTURE
The algorithm
algorithm needs
needs to
to be
be generalized
generalized across
across various
various thickness
thickness and
and compositions
compositions
The
by
training
the
network
appropriately.
Possibilities
of
applications
to
situations
involving
by training the network appropriately. Possibilities of applications to situations involving
particulate
composites
can
be
investigated.
particulate composites can be investigated.
ACKNOWLEDGEMENTS
ACKNOWLEDGEMENTS
We
register our
gratitude to
to Dr
Dr Satish
Satish Udpa
Udpa and
and his
his team,
team, atat Michigan
Michigan State
State
We register
our gratitude
University
for
the
invaluable
guidance
and
help
extended
to
us.
We
thank
all
the
staff
and
University for the invaluable guidance and help extended to us. We thank all the staff and
students
at
CNDE,
IIT
Madras
for
their
assistance.
students at CNDE, IIT Madras for their assistance.
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