Acoustic emission source positioning research of 3D braided composite material based on the wavelet network
The acoustic emission detection technology is used to position the acoustic emission source of 3D braided composite material. Through comprehensive utilization of the characteristic parameters of acoustic emission signals, the wavelet neural network (WNN) is used to conduct damage positioning and co...
Saved in:
| Published in: | Functional Materials |
|---|---|
| Date: | 2016 |
| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Published: |
НТК «Інститут монокристалів» НАН України
2016
|
| Subjects: | |
| Online Access: | https://nasplib.isofts.kiev.ua/handle/123456789/120633 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Journal Title: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Cite this: | Acoustic emission source positioning research of 3D braided composite material based on the wavelet network / Su Hua, Zhang Tianyuan, Zhang Ning // Functional Materials. — 2016. — Т. 23, № 2. — С. 331-336. — Бібліогр.: 8 назв. — англ. |
Institution
Digital Library of Periodicals of National Academy of Sciences of Ukraine| _version_ | 1860249858050359296 |
|---|---|
| author | Su Hua Zhang Tianyuan Zhang Ning |
| author_facet | Su Hua Zhang Tianyuan Zhang Ning |
| citation_txt | Acoustic emission source positioning research of 3D braided composite material based on the wavelet network / Su Hua, Zhang Tianyuan, Zhang Ning // Functional Materials. — 2016. — Т. 23, № 2. — С. 331-336. — Бібліогр.: 8 назв. — англ. |
| collection | DSpace DC |
| container_title | Functional Materials |
| description | The acoustic emission detection technology is used to position the acoustic emission source of 3D braided composite material. Through comprehensive utilization of the characteristic parameters of acoustic emission signals, the wavelet neural network (WNN) is used to conduct damage positioning and computation, and by combining the shuffled frog leaping algorithm (SFLA), it can improve the convergence performance. Through experiment comparison with traditional positioning computation method, after optimization with the frog leaping algorithm, the wavelet network acoustic emission source positioning method can effectively improve the precision of damage positioning.
|
| first_indexed | 2025-12-07T18:41:48Z |
| format | Article |
| fulltext |
Functional materials, 23, 2, 2016 331
ISSN 1027-5495. Functional Materials, 23, No.2 (2016), p. 331-336
doi:http://dx.doi.org/10.15407/fm23.02.331 © 2016 — STC “Institute for Single Crystals”
Acoustic emission source positioning research
of 3D braided composite material based on the
wavelet network
Su Hua, Zhang Ning, Zhang Tianyuan
Tianjin Polytechnic University, Binshui Road 399, Xiqing District,
Tianjin, 300387, P.R. China
The acoustic emission detection technology is used to position the acoustic emission source of
3D braided composite material. Through comprehensive utilization of the characteristic param-
eters of acoustic emission signals, the wavelet neural network (WNN) is used to conduct damage
positioning and computation, and by combining the shuffled frog leaping algorithm (SFLA), it
can improve the convergence performance. Through experiment comparison with traditional
positioning computation method, after optimization with the frog leaping algorithm, the wavelet
network acoustic emission source positioning method can effectively improve the precision of
damage positioning.
Keywords: Three-dimensional (3D) Braided Composites, shuffled frog leaping algorithm
(SFLA),Wavelet Neural Network (WNN), Acoustic Emission Source Positioning
Технология детектирования акустической эмиссии используется для определения по-
ложения источника акустической эмиссии трехмерных композитных материалов с упроч-
няющей оплеткой. Путем всестороннего использования характеристических параметров
сигналов акустической эмиссии, вейвлет-нейросети используются для определения и пози-
ционирования дефекта, а комбинируя с алгоритмом неупорядоченных прыжков лягушки,
можно улучшить сходимость. Путем сравнения экспериментов с традиционным методом
расчета позиционирования, после оптимизации алгоритмом прыгающей лягушки, метод
вейвлет-нейросетей для позиционирования источника акустической эмиссии может эффек-
тивно повысить точность определения положения дефекта.
Дослідження позиціонування джерела акустичної емісії тривимірних
композитних матеріалів зі зміцнювальним оплетенням на основі вейвлет-мереж.
Су Хуа, Чжан Нін, Чжан Тяньюань
Технологію детектування акустичної емісії використано для визначення положення
джерела акустичної емісії тривимірних композитних матеріалів зі зміцнювальним оплетен-
ням. Шляхом всебічного використання характеристичних параметрів сигналів акустичної
емісії, вейвлет-нейромережі використовують для визначення та позиціонування дефектів, а
завдяки поєднанню з алгоритмом невпорядкованих стрибків жаби можна покращити сход-
ження. Шляхом порівняння експериментів з традиційним методом розрахунку позиціону-
вання, після оптимізації алгоритмом стрибків жаби, метод вейвлет-нейромереж для пози-
ціонування джерела акустичної емісії може ефективно підвищувати точність визначення
положення дефектів..
1. ������������������������
The acoustic emission source positioning is
an important work of acoustic emission detec-
tion technique, which is an important index to
evaluate the acoustic emission detection, and
its accuracy reflects whether the position ob-
tained through acoustic emission detection is
consistent with the actual position where the
defect is found. How to increase the position-
332 Functional materials, 23, 2, 2016
Su Hua et al. / Acoustic emission source positioning research ...
ing accuracy of acoustic source and maximally
reduce missed or false positioning is an impor-
tant task during acoustic emission source po-
sitioning. Due to different aeolotropic sound
velocity, the application of tradition geometri-
cal time-difference positioning method for aeo-
lotropic composite material is restricted. In re-
cent years, in accordance with the aeolotropy of
composite material, several acoustic emission
source positioning methods based on geometri-
cal time-difference positioning method have
been developed. For instance, after fitting aeo-
lotropic wave velocity, it can be used as known
parameter, and added to the classic geometri-
cal positioning method and virtual wave front
method [1,2,3].
The acoustic emission signal from composite
material during loading can include all infor-
mation of composite material damage, such as
the location of damage source, damage model
and degree. It has broad application prospects
to use the acoustic emission technology to de-
tect the structure of composite material.
This paper proposes an acoustic emission
detection method of optimized wavelet neural
network (WNN) based on the shuffled frog leap-
ing algorithm (SFLA). By combining acoustic
emission and WNN, this paper conducts theo-
retic analysis and specific experiment research
on the acoustic emission source positioning of
3D braided composite material.
2. WNN based on shuffled frog leaping
algorithm
2.1 Wavelet network
The wavelet neural network (WNN) is the
product by combining the wavelet analysis
theory and neural network theory. The wavelet
transform (WT) conducts multi-scale analysis of
signal through scale expansion and translation,
which can effectively extract local information
of signal; the neural network has the character-
istics of self-learning, self-adaption and fault
tolerance, which is also a common function ap-
proximator. The elements and whole structure
of WNN are determined in accordance with the
wavelet analysis theory, which can avoid the
blindness of BP neural network in structural
design. In addition, it has stronger learning
ability and higher accuracy, and for the same
learning task, the WNN has simpler structure
and faster convergence rate. [7,8] This paper
chooses a compact WNN, and its structure is as
shown in Fig.1.
Dimension design for input and output. The
dimension of input and output should be de-
termined in accordance with the problem that
needs to be solved and data expression method.
In order to conduct accurate positioning of the
source of acoustic emission signal, in addition
to obtaining traditional time of arrival, we also
need to comprehensively consider the sound
wave information such as energy, amplitude
and counts to conduct positioning training in a
clearer and more comprehensive way. Because
all the input signals are simulation signals,
which requires sampling before training, the di-
mension of input layer should be determined in
accordance with the sampling point number of
waveform, which should be finally determined
by coordinating the positioning accuracy. The
dimension of output layer should also be deter-
mined in accordance with actual requirement.
The dimension of output layer should also be
determined with actual requirement. This pa-
per mainly studies the source positioning of
acoustic emission signal, so the node number of
input layer is set as 8, and the nerve cell num-
ber at output layer is set as 2, which are the
abscissa and ordinate respectively.
Excitation function. By combining the char-
acteristics of damage acoustic emission signal
of 3D braided composite material, this paper
chooses the Morlet wavelet as the network ex-
citation function:
ψ ( ) cos( . ) exp( . )x x x= -1 75 0 5 2 (1)
Through translation and expansion of ψ(x), we
can obtain the j(j = 1,2,...,J)th wavelet function
on the hidden layer of WNN:
ψ ψj
j
j
j
j jx
a
x b
a
a b( ) ,=
-æ
è
ççççç
ö
ø
÷÷÷÷÷
Î Â1 (2)
Hidden layer and node selection. The node
number on hidden layer is very important to the
performance of whole network. If the number
is too low, the network won’t obtain adequate
Fig. 1. Three-layers network structure
Functional materials, 23, 2, 2016 333
Su Hua et al. / Acoustic emission source positioning research ...
information from the sample, which cannot suf-
ficiently reflect the internal rule of sample, and
it will further reduce the network’s generaliza-
tion ability; if the number is too high, it will
increase the network’s learning time, which
will cause the decrease in convergence rate.
At present, there is no explicit analytic expres-
sion that can be used to select node number of
hidden layer, which is generally determined by
combining the actual problem through multiple
experiments. By combining the actual situa-
tion, in accordance with the error precision re-
quirement, this paper sets the node number of
hidden layer as 17.
2.2 SFLA
The shuffled frog leaping algorithm (SFLA)
is a population-based cooperative search meth-
od inspired by biological emulation in the na-
ture[6]. Its basic idea is: F frogs form the ini-
tial frog population, in which, the solution of
ith frog in S dimension solution space can be
expressed as X x x xi s= ( , ,... ,)1 2 . Then, sort
individual frogs in the initial frog population
in descending order in accordance with the
adaptive value, and find the optimal solution
Px for the initial frog population. Later, divide
the frog population into m sub-populations, and
each sub-population contains n frogs, which sat-
isfy the relation F = m×n. When the first frog
enters the first sub-population, the second frog
enters the second sub-population, the mth frog
enters the mth sub-population, and the (m + 1)th
frog enters the first sub-population, until all
frogs have entered the specified sub-popula-
tion. In Formula (3), j refers to the jth frog, while
Pj refers to the probability of the jth frog being
chosen. Then, we can find the best solution Pb
and worst solution Pw in the sub-population; in
accordance with Formulas (4) and (5), we can
conduct local deep search to each sub-popula-
tion [7,8].
P n j n n j nj = + - + =2 1 1 1 2( ) /[ ( )], , ,..., (3)
S Rand P Pb w= ´ -() ( ) (4)
P P S S S Snw w= + - £ £,( )max max (5)
In Formula (4), Rand() refers to a random
number between 0 and 1; S refers to the frog’s
leap step size, which is a reasonable difference
value; Smax: maximum step size of each move-
ment made by single frog; Pnw refers to the up-
dated Pw. If Pw is in a feasible solution space,
calculate corresponding fitness of Pnw. If the
corresponding fitness of Pnw is less optimal than
the corresponding fitness of Pw replace Pb in
Formula (4) with Px to update Pw; if there still
is no progress, randomly generate a new frog to
replace Pw. Otherwise, repeat the update pro-
cess until it reaches the preset local iterations
LS. After completing deep search of various
sub-populations, remix and sort the frogs in
the frog population; then, divide the frog popu-
lation into various sub-populations to continue
local search, until it satisfies any preset condi-
tion to stop the algorithm.
In this paper, the SFLA is used to optimize
the WNN, the initial operation parameters are
set as:
F: the population size is 100, which refers to
the total quantity of individuals contained in
the population.
m: the sub-population size is 20.
Smax: the maximum step size of each move-
ment made by single frog is 20.
LS: the local evolution times are 10.
SF: the glocal evolution times are 400.
3. Experimental
3.1 Experimental sample
The TORAYCCA@ T300 carbon fiber (12K)
is used as the reinforced fiber for the plate spec-
imen of 3D braided composite material. The
braided structure of fabricated part is four-step
1×1 3D 5-direction braided structure, and the
basis material is TDE86# epoxy resin. The res-
in transfer molding (RTM) process is used for
curing molding, and the sample has a thickness
of (5±0.1)mm. In the experiment, 6 samples of
the same size (35cm×20cm×5mm) are chosen
and divided into Group A and Group B, with 3
samples in each group; Group A is used in the
lead-break experiment, while Group B is used
in the damage positioning experiment.
3.2 Four-probe array plane positioning
method
This test adopts the four-probe array plane
positioning method to position the acoustic
emission source, as shown in Fig.2.
Arrange the four probes into a rhombus on
the surface of test piece, the time difference of
probes S1 and S3 in receiving the acoustic emis-
sion signals is ∆tx, while the time difference of
probes S2 and S4 in receiving the acoustic emis-
sion signals is ∆ty. The distance between S1 and
S3 is a, while the distance between S2 and S4
is b, and the wave velocity is V. Therefore, the
AE source is located at the intersection point
Q(X,Y) between two curves, and the coordinate
expressions are as shown in Formulas (6) and
(7).
X
t V
a
t V X
a
Yx
x=
D
D + -
æ
è
ççç
ö
ø
÷÷÷÷ +
æ
è
çççççç
ö
ø
÷÷÷÷÷÷÷2
2
2
2
2 (6)
334 Functional materials, 23, 2, 2016
Su Hua et al. / Acoustic emission source positioning research ...
Y
t V
b
t V Y
a
Xy
y=
D
D + -
æ
è
ççç
ö
ø
÷÷÷÷ +
æ
è
çççççç
ö
ø
÷÷÷÷÷÷÷2
2
2
2
2 (7)
3.3 Result and analysis of simulative
lead-break experiment
Conduct four-point positioning experiment
to the plate test piece of 3D four-direction
braided composite material in Group A.
Fig.3 shows the diagram of four-direction
plane lead-break experiment, and the four sen-
sors are arranged into a rhombus. The standard
lead-break experiment is used to simulate the
source which generates the acoustic emission
signals, and take the average value of three re-
sponses. In the experiment, with the rhombus
center as the origin, and the coordinates of four
sensors are (0,15); (0,–15); (–30,0) and (30,0)
respectively. Select four test points with the co-
ordinates of (15,10), (–15,–10), (–5,5) and (5,–5)
respectively, conduct 3 experiments at each
test point, and calculate the average value.
Use traditional mathematical morphology to
conduct filtering processing of the experiment
data obtained above; then, use the correlation
analysis method to extract critical values of
time difference; conduct computation to obtain
the coordinate location of acoustic emission sig-
nals. In the meantime, for the convenience of
comparison, the positioning analysis method
that combines the WNN and SFLA is adopted,
which integrates multiple other parameters,
including the time difference parameter, into
the positioning algorithm to obtain better posi-
tioning result. Because the experiment results
of test pieces in Group A are close, Test piece
1is used as the example to analyze the experi-
ment result. Table 1 shows the experiment
data of Test piece 1. n accordance with the ex-
periment results, we can see that when single
parameter time difference is used to position
the test point, even after mathematic morpho-
logical filtering and correlation analysis, its
positioning accuracy is still low. The absolute
average error of its abscissa is 2.1 cm, while
Fig. 2. Four-probe array AE source positioning
method
Fig.3 Sensor layout on the plate test piece of 3D
four-direction braided composite material dur-
ing lead-break experiment
Table 1 Four-point positioning result comparison for Test piece 1 in Group A
Four-point positioning comparison of 3D braided composite material
Test point (cm)
WNN + SFLA positioning method Correlation analysis method
Positioning value
(cm) Error (cm) Positioning value
(cm) Error (cm)
No. x y x y x y x y x y
1 15 10 16.5 11.6 1.5 1.6 12.7 12.1 2.3 2.1
2 15 10 13.4 11.3 1.6 1.3 13.1 12.4 1.9 2.4
3 15 10 16.4 11.5 1.4 1.5 12.5 12.5 2.5 2.5
4 –15 –10 –13.7 –8.7 1.3 1.3 –13.5 –8.7 1.5 1.3
5 –15 –10 –16.4 –11.6 1.4 1.6 –12.4 –7.9 2.6 2.1
6 –15 –10 –13.5 –11.5 1.5 1.5 –12.6 –7.6 2.4 2.4
7 5 –5 6.3 –3.6 1.3 1.4 3.2 –6.8 1.8 1.8
8 5 –5 6.8 –3.5 1.8 1.5 2.9 –7.1 2.1 2.1
9 5 –5 3.8 –3.8 1.2 1.2 2.8 –6.9 2.2 1.9
10 –5 5 -3.4 6.5 1.6 1.5 –3.2 6.6 1.8 1.6
11 –5 5 -6.3 6.4 1.3 1.4 –2.9 7.1 2.1 2.1
12 –5 5 -3.5 6.5 1.5 1.5 –3.0 7.2 2 2.2
Functional materials, 23, 2, 2016 335
Su Hua et al. / Acoustic emission source positioning research ...
absolute average error of its ordinate is 2.04,
which is caused by the anisotropy of composite
material in acoustic wave propagation velocity.
In the meantime, the unique characteristics of
braided angle also affect the measured value of
velocity of the acoustic emission signals, while
this also directly affects the measurement pre-
cision of time difference, which causes big error.
When the WNN and SFLA are combined to pro-
cess data and conduct positioning analysis, the
absolute average error of its abscissa is 1.45cm,
while absolute average error of its ordinate is
1.44. We can see that the positioning error has
declined by 5mm, and the positioning accuracy
has been significantly improved.
3.4 Result and analysis of damage posi-
tioning experiment of 3D braided compos-
ite material
Test pieces in Group B is chosen for the ex-
periment, and for the convenience to compare
the test results, we artificially created a dotted
surface damage on each test piece in advance,
as shown in Fig.4. We place the sensors on
the coordinates of (0,15); (0,–15); (–30,0) and
(30,0) respectively. In this paper, Test piece 1
in Group B is chosen for analysis. After causing
dotted damage at coordinate (–13, 6) on the test
piece surface, stress is imposed on the test piece
for 8 consecutive times through the impact test
machine, and the acoustic emission signals are
measured and recorded. By adopting the posi-
tioning method that combines WNN and SFLA,
the coordinate position of damage is calculated,
as shown in Table 2.
In accordance with the acoustic emission
positioning experiment of 3D braided com-
posite material, we can see that based on the
traditional positioning method, by adding
morphological filtering, correlation processing
and related neural network technology based
on SFLA, we can filter the mechanical noise,
conduct multi-factor determination of relative
time difference and study the multi-parameter
self-learning positioning method, and great re-
sults have been achieved. During the damage
positioning, the absolute average error of its
abscissa is 1.45cm, while absolute average error
of its ordinate is 1.44, which is consistent with
the error range of Group A during lead-break
experiment. The damage assessment points
present even distribution with the damage
point as the center, as shown in Fig.5. In Fig.5,
the round spot represents the actual damage
position, while the asterisk represents the neu-
ral network and frog leaping tagged position.
In accordance with Fig.5, we can see that by
adopting the positioning method that combines
WNN and SFLA, the positioning accuracy has
been significantly improved.
4. Conclusion
During acoustic emission positioning re-
search on the damage of 3D braided compos-
ite material, through experiemnt, it can verify
that: by adopting the positioning method that
combines WNN and SFLA, the positioning ac-
curacy has been significantly improved. Com-
pared to the traditional positioning analysis
Table 2 Actual damage positioning results of
Test piece1 in Group B
Times
Actual co-
ordinates
(cm)
Computed
coordinates
(cm)
Error (cm)
x y x y x y
1 –13 6 –11.5 7.7 1.5 1.7
2 –13 6 –11.4 7.3 1.6 1.3
3 –13 6 –14.8 4.7 1.8 1.3
4 –13 6 –14.4 4.4 1.4 1.6
5 –13 6 –11.2 7.5 1.8 1.5
6 –13 6 –11.6 4.9 1.4 1.1
7 –13 6 –14.4 7.6 1.4 1.6
8 –13 6 –14.5 7.4 1.5 1.4
Fig. 4. Damage position of 3D braided composite
material
Fig.5 Actual damage position comparison of 3D
braided composite material
336 Functional materials, 23, 2, 2016
Su Hua et al. / Acoustic emission source positioning research ...
method, the positioning error has declined by
5mm on average.
Due to the impact of various factors of 3D
braided composite material on the extraction of
acoustic emission signals, such as its braided
angle, fiber saturated level and resin infiltra-
tion/curing, it cannot realize accurate position-
ing of damage.
Refe�e��es
1. Wan ZhenKai, J. Text. Res., 28, 53, 2007
2. Gu Haibei, Liu Wugang, Sun Fei, Zhang Kai,
Missiles and Space Vehicles of Chine, 1, 49,
2012.
3. Liu Zhidong, Pang Baojun, Tang Qi., Piezoelec-
trics & Acoustooptics, 32, 493, 2010.
4. Huang XiangSheng, Xiao HanBin, Port Engi-
neer.Techn., 3, 13, 2008
5. Cai QiYing, Lin JiangHua, J. Vibr. Shock., 21(3),
11, 2002.
6. Feng H, Liang R Y, Li D X, et al.First IEEE In-
ternational Conference on Information Science
and Engineering. IEEE Computer Society,
p. 3600,2009.
7. Rao R M, Lakshmi. J, Comp. Mater., 46 (24):
3031, 2012.
8. Bijami E, Shahriari-Kahkeshi M, Zekri M., In-
ternational Conference on Intelligent Informa-
tion Technology Application. 2010.
|
| id | nasplib_isofts_kiev_ua-123456789-120633 |
| institution | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| issn | 1027-5495 |
| language | English |
| last_indexed | 2025-12-07T18:41:48Z |
| publishDate | 2016 |
| publisher | НТК «Інститут монокристалів» НАН України |
| record_format | dspace |
| spelling | Su Hua Zhang Tianyuan Zhang Ning 2017-06-12T14:29:39Z 2017-06-12T14:29:39Z 2016 Acoustic emission source positioning research of 3D braided composite material based on the wavelet network / Su Hua, Zhang Tianyuan, Zhang Ning // Functional Materials. — 2016. — Т. 23, № 2. — С. 331-336. — Бібліогр.: 8 назв. — англ. 1027-5495 DOI: dx.doi.org/10.15407/fm23.02.331 https://nasplib.isofts.kiev.ua/handle/123456789/120633 The acoustic emission detection technology is used to position the acoustic emission source of 3D braided composite material. Through comprehensive utilization of the characteristic parameters of acoustic emission signals, the wavelet neural network (WNN) is used to conduct damage positioning and computation, and by combining the shuffled frog leaping algorithm (SFLA), it can improve the convergence performance. Through experiment comparison with traditional positioning computation method, after optimization with the frog leaping algorithm, the wavelet network acoustic emission source positioning method can effectively improve the precision of damage positioning. en НТК «Інститут монокристалів» НАН України Functional Materials Technology Acoustic emission source positioning research of 3D braided composite material based on the wavelet network Article published earlier |
| spellingShingle | Acoustic emission source positioning research of 3D braided composite material based on the wavelet network Su Hua Zhang Tianyuan Zhang Ning Technology |
| title | Acoustic emission source positioning research of 3D braided composite material based on the wavelet network |
| title_full | Acoustic emission source positioning research of 3D braided composite material based on the wavelet network |
| title_fullStr | Acoustic emission source positioning research of 3D braided composite material based on the wavelet network |
| title_full_unstemmed | Acoustic emission source positioning research of 3D braided composite material based on the wavelet network |
| title_short | Acoustic emission source positioning research of 3D braided composite material based on the wavelet network |
| title_sort | acoustic emission source positioning research of 3d braided composite material based on the wavelet network |
| topic | Technology |
| topic_facet | Technology |
| url | https://nasplib.isofts.kiev.ua/handle/123456789/120633 |
| work_keys_str_mv | AT suhua acousticemissionsourcepositioningresearchof3dbraidedcompositematerialbasedonthewaveletnetwork AT zhangtianyuan acousticemissionsourcepositioningresearchof3dbraidedcompositematerialbasedonthewaveletnetwork AT zhangning acousticemissionsourcepositioningresearchof3dbraidedcompositematerialbasedonthewaveletnetwork |