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JOURNAL OF APPLIED SCIENCES RESEARCH
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2013 December; 9(13): pages 6288-6294.
Published Online: 15 January 2014.
Research Article
Fatigue Crack Characterization In Epoxy L160 By Fuzzy Clustering Means On
Acoustic Emission Data
Jahan Taghizadeh
1
Assis. Prof., Mechanical Engineering Faculty, Qom University of Technology, Qom, Iran.
Received: 12 November 2013; Revised: 14 December, 2013; Accepted: 20 December 2013.
© 2013
AENSI PUBLISHER All rights reserved
ABSTRACT
The fatigue crack growth characteristics of structural epoxyL160 are analyzed using quantitative acoustic emission (AE) technique.
This was experimentally investigated by three-point bending testing of specimens under low cycle constant amplitude loading using the
Fuzzy clustering Means. The crack growth sequence, that is, initiation, crack propagation, and fracture, is extracted from their
corresponding frequency feature bands, respectively. The results obtained proved to be superior to qualitative AE analysis and the
traditional linear elastic fracture mechanics for fatigue crack characterization in structural epoxyL160.
Key words: EpoxyL160; Acoustic Emission; Fuzzy clustering Means; Fatigue,
INTRODUCTION
Early detection of fatigue crack-growth in
epoxyL160 structures is an ongoing challenge.
Furthermore, characterization of the different stages
of the fatigue lifecycle using NDE techniques is
particularly difficult. AE systems have been shown
to serve as early damage detection mechanisms in
bridge structures. This technology, however, is
fraught with noise problems and complex datasets
that are difficult to interpret. This paper attempts to
design and implement a data mining scheme that can
classify raw AE datasets into discrete clusters using
an improved variant of the popular Fuzzy clustering
means algorithm. Linear elastic fracture mechanics
(LEFM) is a useful tool for characterizing crack
growth by fatigue and on other hand application of
fracture mechanics to fatigue problems has become a
fair routine [1]. Acoustic emission technology is the
most appropriate and useful nondesytructive testing
(NDT) method for studying fatigue cracks growth in
civil engineering structure because it can monitor its
health in real time [2]. Effective crack detection may
lead to an early warning. The AE technique can be
used to continuously detect slight deformation and
damage in the interior of materials. In other words,
sampling AE signals and analyzing their
characteristics may contribute to the understanding
of the real-time failure behavior of materials [3]. The
AE parametric analyses have been commonly
employed
during
fatigue
crack
growth
characterization. Ohtsu and Tomoda [4]. reported
that the AE waveform shape depends on the cracking
mode, enabling the classification of cracks in
different materials. Shear cracks generally follow
tensile as the material approaches to final failure.
reported that failure phenomena in metals can be
interpreted by evaluating the amplitude distribution,
AE event count, and total AE energy [5]. Discussed
the application of other AE parameters, such as rise
angle (RA) value, rise time (RT), AE hit rate, and
duration damage characterization of metal [6]. They
realized that as the duration and RT increase, there is
a shift of cracking mode from tensile to shear. Any
articles correlated the AE parameters like rise time
and duration with corrosive processes in aluminums
[7].
A good correlation between AE parameters and
fracture mechanics principles during fatigue has been
reported by [8,9]. Grosse et al reported the pros and
cons of the parametric AE analysis. They postulated
that in practical applications it can be difficult to
discriminate an AE signal from noise after the signal
has been reduced to a few parameters.
Quantitative techniques that deal with the study
of AE signal waveform have been applied in various
engineering fields for damage evaluation. The fast
Fourier transform (FFT) has been used to decompose
a time-domain sequence in terms of a set of basic
functions. A major problem in using the FFT results
from the fact that the transform is the result of
integration in the continuous time domain over the
entire signal length [11,13]. This problem led to the
evolution of the time-frequency data processing
Corresponding Author: Jahan Taghizadeh, Assis. Prof., Mechanical Engineering Faculty, Qom University of Technology,
Qom, Iran.
P.O.B. 37195-1519 Qom, [email protected]; (Phone: +25-36641601; fax: +25-36641604)
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Jahan Taghizadeh, 2013 /Journal Of Applied Sciences Research 9(13), December, Pages: 6288-6294
methods, such as the short time Fourier transform
(STFT). Neild et al provided a thorough review of
various time-frequency techniques for structural
vibration analysis. The wavelet transform which is
the main interest of this paper has been successfully
combined with AE signal parameter for analysis of
real-time failure process, such as differentiation of
crack types, quantification of damage, and
identification of AE source locations [15]. This paper
discusses the fatigue crack growth characterization in
structural epoxyL160 and weld using quantitative
methods. The frequency feature bands corresponding
to crack growth sequence, that is, initiation, crack
propagation, and fracture, are extracted and
compared with qualitative AE analysis and LEFM.
Fuzzy clustering technique is used to classify
the AE signal to different sources of signals. FCM
has the ability to discover the cluster among the
data, even when the boundaries between the
subgroup are overlapping, FCM based technique has
an advantage over conventional statistical technique
like maximum likelihood estimate, nearest neighbor
classifier etc, because they are distribution free (i.e.)
no knowledge is required about the distribution of
data.
In the approach, the aim is of clustering is to
determine the cluster centers, which are
representative values of features corresponding to
the classified categories. Once clustering centers are
determined at the learning stage, the classification is
made by the comparison of the incoming pattern and
each clustering center [16].
X = { X 1 , X 2 ,..., X 3 } ⊂ R where
X i = ( xi1 , xi 2 ,..., xis ) ∈ R
each
is a feature vector; xij
is the jth feature of individual xi . For each integer
c, 2 ≤ c  n , let Vcn be the vector space of c × n
matrices with entries in
ij th
element
function u i
[0,1] , and let uij
of
: X → [0,1]
any
denote the
U ∈ Vcn .
The
becomes a membership
function and is called a fuzzy subset in X . Here
u ij = u i ( x j )
subset of Vcn .
M fc = {U ∈ Vcn u ij ∈ [0,1]∀i, j;
c
∑u
ij
= 1∀j;
i =1
Each
U ∈ M fc
c
∑ u 〉0∀i }
(6)
ij
i =1
Is called a fuzzy c-partition
of X ; M fc is the fuzzy c-partition space associated
with X . For any real number m ∈
real-valued
functional
n
[1,6], define the
J : M fc × Lc → R
c
J (U , V ) = ∑∑ (uik ) m X k − U i
by
2
k =1 i =1
Fuzzy clustering Means:
Let
suppose that there are n samples, which can be
divided into c classes. Consider the following
is called the grade of membership
of x j in the fuzzy set u i . In the space of samples, we
Table 1: Mechanical property of epoxy
Resin
Density (g/cm3)
EpoxyL160
2.6
1 ≤ M  ∞ , and usually m = 2 where U = {uik }
is the member- ship function, with uik ∈ [0,1] ,
which denotes the degree of membership of the k th
pattern and i th cluster centers; V = {v1 , v2 ,..., vc }
is a vector of c cluster. These
vi
are interpreted as
clusters defined by their companion U matrix, and
ply a functional role in our development. The
functional J is a weighted, least squares objective
function. In order to obtain the optimum fuzzy
partition, this objective function must be minimized.
Experimental Details:
- Specimens Design:
Received epoxyL160 was supplied in the
standard thermo mechanical heat treatment condition
in the form of 16 mm thickness plates. The standard
three-point bending specimens were designed from
the epoxy, as a representative part of a epoxyL160
bridge in accordance with ASTM
E 647 standards.
The mechanical properties of the specimens used are
shown in Tables 1, respectively. The specimens were
notched using electrical discharge machining (EDM)
to an initial crack length of mm.
18 The specimen
was fatigue pre cracked using the MTS machine at a
frequency of Hz
15 of length 0.6 mm. The
specimens’ surfaces were mechanically polished by
grinding and buffing to permit observations of the
crack path. The detailed geometry of the specimen is
illustrated in Figure 1.
Tensile failure (N/mm)
80
Elongation (%)
3
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Jahan Taghizadeh, 2013 /Journal Of Applied Sciences Research 9(13), December, Pages: 6288-6294
Fig. 1: Specimen geometry
Mechanical testing and equipment:
A servo hydraulic testing machine with a
maximum load capacity of kN
250 (Hiwa Co.,
Tehran, Iran) was used for the fatigue tests at an
ambient temperature of 300 K. The specimens were
tested under sinusoidal cyclic loading at a frequency
of 7.5 Hz. The specimens were tested with different
peak loads (16 kN and 10 kN) at a load R -ratios of
0.1. At least three specimens were tested under each
condition in other to ensure regularity in the
experiment. Damage initiation and progress in the
specimens monitored by an AE system. Preliminary
to damage check, the data acquisition system
calibrated for each kind of specimens, according to a
pencil lead break procedure. At the same time,
velocity and attenuation of the AE waves can
measure. For that, the lead breakage operation was
repeated several times and at different locations
between the sensors. The difference in arrival times
on the sensors deduced. Furthermore, each waveform
digitized and stored. After storage and before
processing, the signals subjected to a linear location
procedure to determine the location of the AE source.
The AE signals were detected by using 2
broadband piezoelectric sensors with frequency
range of 10 kHz to 2 MHz. Vaseline was used at the
interface between the sensors and the specimen
surface to obtain proper signals. A preamplifier of
40 db gain was used to capture the AE signals. The
crack-tip opening-displacement gauge (CTOD) was
used to monitor the fatigue crack growth in the
structure.
Results and Discussions
- Linear Elastic Fracture Mechanics:
Discovered the power-law relationship for the
fatigue crack growth and proposed an exponent of 4
for the constant after series of experiments. Figures 2
and 3 respectively, shows the relationship between
the fatigue crack growth rates and stress intensity
factor ranges and crack length and number of cycles
under different load ratios.
Fig. 2: Relationships between crack growth rates and stress intensity factor range
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Jahan Taghizadeh, 2013 /Journal Of Applied Sciences Research 9(13), December, Pages: 6288-6294
Fig. 3: Relationships between crack length and number of cycles.
In nearly the whole fatigue lives, we obeyed the
Paris law (7) for the epoxyL160 where is a
representative crack length, is the number of fatigue
cycles, is the applied stress intensity factor range,
and assumed to be constants for a particular material.
However, the crack growth rates were increased in
the weld relative to in the base metal, suggesting that
the cracks propagated more rapidly in the weld due
to the changes in the microstructure. We realized
from the diagram that peak load was less significant
on the crack propagation rate.
- Acoustic Emission
Propagation:
during
Fatigue
Crack
signals so generated. Figure 4 shows the
relationships between the cumulative AE counts rates
and stress intensity factor ranges for the welded
specimens under different peak loads. The AE counts
rates increased in a linear relationship with the
increase in on the log-log axes, which is well
consistent . Compared to the results from LEFM, the
peak load had influence on the welded specimen.
Moreover, higher AE counts rates were also
observed in the welded specimens than in the base
metal specimens. In addition, the slopes of the lines
for the welded specimens were somehow higher than
the base metal specimens, also suggesting that the
weld generated more AE signals during fatigue crack
propagation.
The fatigue crack growth characteristics can be
analyzed by studying the parameters of the AE
Fig. 4: Relationship between cumulative count rate and stress intensity factor range.
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FCM Packet Analysis:
The characteristics of fatigue crack propagation
during the three-point bending testing of the
epoxyL160 beam are classified under 3 stages: crack
initiation, crack propagation, and failure at various
peak loads corresponding to regions I, II, and III,
respectively.
Figures5 show the AE wave in region 1 which
corresponds to fatigue source initiation for epoxy.
The waveform is low amplitude, wide pulse with a
narrow frequency scale, mostly located at 80
kHz to
180 kHz. At this stage, the AE signals are generated
by the formation of crack source and plastic
deformation on the tip of the notch which generate
intense AE events. Higher amplitudes are recorded
for epoxyL160 due to the release of residual stresses.
Figures6 show the AE in region 2 corresponding to
fatigue crack propagation. The waveform at this
stage as compared to the fatigue source has high
amplitude, narrow pulse, with a wide energy scale,
and a main frequency scale of 250
kHz to 350 kHz
for the epoxy.
Fig. 5: Fatigue crack initiation in epoxy
Fig. 6: Fatigue crack propagationin epoxy
Figure7 show the AE waveform at the rapid
crack propagation stage; the energy of the AE signal
increased until the specimen completely failed, and
the AE waveform amplitude is higher than those of
the earlier stages. The waveform characteristic shows
that this type of AE signal is a burst signal. The
fracture waveform is a high amplitude narrow pulse,
with a wide energy scale, and a main frequency scale
of 300 kHz to 400 kHz for the epoxy.
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Fig. 7: Fracture in epoxy
Conclusion:
From the above discussions, it is evident that
fatigue crack growth rates for the welded specimen
are higher than the base metal; this was enhanced by
the presence of inclusions and heterogeneous
microstructure of the welds. The effect of peak load
on fatigue characterization was found to be
insignificant. Furthermore, the quantitative technique
of the wavelet transform provided clear results for
crack propagation characterization in both the
epoxyL160 and welded specimen.
6.
7.
8.
References
1.
2.
3.
4.
5.
Yanning, Zhao., Mouhua, Wang., Zhongfeng,
Tang., Guozhong, Wu, 2011. "Radiation effects
of UHMW-PE fibre on gel fraction and
mechanical properties". J. Radioanal. Nucl.
Chem, 80: 274-277.
Alcock, B., N.O. Cabrera, N.M. Barkoula, J.
Loos, T. Peijs, 2006. " The mechanical
properties of unidirectional all-polypropylene
composites". Appl Sci and Manufacturing, 37:
716-726.
Brian, M., A. Brittany, F. Brian, S. David, 2009.
"Weight reduction and cost savings using hybrid
composites
containing
high
modulus
polypropylene fiber". COMPOSITES &
POLYCON, pp. 15-17. American Composites
Manufacturers Association, Tampa, FL USA
Ogita, T., Y. Kawahara, Y. Soga, M. Matsuo,
1992. "Morphology and mechanical properties
of ultrahigh molecular weightpolypropylene
prepared by gelatin/crystallization at varios
temperatures". Colloid polym, 270: 833-839.
Bussiba, A., M. Kupiec, S. Ifergane, R. Piat, T.
Bohlke, 2008. "Damage evolution and fracture
events sequence in various composites by
acoustic emission technique". Compos Sci and
Technol, 68: 1144-1155.
9.
10.
11.
12.
13.
14.
Loutas, T.H., V. Kostopoulos, 2009. "Health
monitoring of carbon/carbon, woven reinforced
composites damage assessment by using
advanced signal processing techniques".
Compos Sci and Technol, 69: 273-283.
Zhuang, X.M., Xiong, Yan., 2005. "
Investigation of damage mechanisms in selfreinforced polyethylene composites by acoustic
emission". Compos Sci and Technol, 66: 444449.
Godin, N., S. Hugueta, R. Gaertnera, 2004.
"Integration of the Kohonen’s self-organising
map and k-means algorithm for the segmentation
of the AE data collected during tensile tests on
cross-ply composites". NDT&E Int, 38: 299309.
Godin, N., S. Hugueta, R. Gaertnera, L.
Salmonb, 2004."Clustering of acoustic emission
signals collected during tensile tests on
unidirectional glass /epoxy composite using
supervised and unsupervised classifiers".
NDT&E Int, 37: 253-264.
Ki Bok, K., Ho Yang, Kang., Dong Jin, Yoon.,
Man Yong, Choi, 2005. "Pattern classification
of acoustic emission signals during wood drying
by principal component analysis and artificial
neural network". Key Eng Mater, 297-300:
1962-1967.
Papas, Y.Z., V. Kostopoulos, 2001. " Toughness
characterization
and
acoustic
emission
monitoring of 2D carbon/carbon ". Engng Fract
Mech, 68: 1557-1573.
Refahi, A., M. Ahmadi, 2010. "Acoustic
Emission
Characteristics
of
Mode
I
Delamination in Glass/Polyester composites". J
Compos Mater, 44: 793.
Johnson, M.,
2002. "Waveform based
clustering and classification of AE transients in
composite laminates using principal component
analysis". NDT&E Int, 35(3): 367-376.
Refahi Oskouei, A., A. Zucchelli, M. Ahmadi,
G. Minak, 2009. ”An integrated approach based
6294
Jahan Taghizadeh, 2013 /Journal Of Applied Sciences Research 9(13), December, Pages: 6288-6294
on acoustic emission and mechanical
information to evaluate the delamination
fracture toughness at mode I in composite
laminate".Materials and design, 32: 1444-1455.
15. Sorensen, B.F., T.K. Jacobsen, 2009. "
Characterizing delamination of fibre composites
by mixed mode cohesive laws". Compos Sci and
Technol, 69: 445-456.
16. Hirai, T., N. Bekris, J.P. Coad, C. Grisolia, J.
Linke, H. Maier, V. Philipps, E. Wessel, 2009.
"Failure modes of vacuum plasma spray
tungsten coating created on carbon fibre
composites under thermal loads". Nucl mater,
392: 40-44.