Acoustic emission data were collected during the hydroburst testing of eleven 15 inch diameter filament wound composite overwrapped pressure vessels. A neural network burst pressure prediction was generated from the resulting AE amplitude data. The bottles shared commonality of graphite fiber, epoxy resin, and cure time. Individual bottles varied by cure mode (rotisserie versus static oven curing), types of inflicted damage, temperature of the pressurant, and pressurization scheme. Three categorical variables were selected to represent undamaged bottles, impact damaged bottles, and bottles with lacerated hoop fibers. This categorization along with the removal of the AE data from the disbonding noise between the aluminum liner and the composite overwrap allowed the prediction of burst pressures in all three sets of bottles using a single backpropagation neural network. Here the worst case error was 3.38 percent

Dion, Seth-Andrew T.

Hill, Eric v. K.

Karl, Justin O.

Spivey, Nicholas S.

Walker, James L., II

NASA Technical Reports Server

¢- 555
NEL_TRAL NETWOP_ BURST PRESSURE pRF, r_1t'TION IN COMPOSITE
OVERWRAPPED PRESSURE VESSELS
ERIC v. K. HILL, SETH-ANDREW T. DION, JUSTIN O. KARL, NICHOLAS S.
SPIVEY, AND JAMES L. WALKER II*
Aerospace Engineering Department, Embry-Riddle Aeronautical University
Daytona Beach, FL 32114
*Non-Destructive Evaluation Branch, NASA Marshall Space Flight Center, AL 35812
Abstract
Acoustic emission data were collected during the hydroburst testing of eleven 15 inch diameter
filament wound composite overwrapped pressure vessels. A neural network burst pressure prediction was
generated from the resulting AE amplitude data. The bottles shared commonality of graphite fiber, epoxy
resin, and cure time. Individual bottles varied by cure mode (rotisserie versus static oven curing), types of
inflicted damage, temperature of the pressurant, and pressurization scheme. Three categorical variables
were selected to represent undamaged bottles, impact damaged bottles, and bottles with lacerated hoop
fibers. This categorization along with the removal of the AE data from the disbonding noise between the
aluminum liner and the composite overleap allowed the prediction of burst pressures in all three sets of
bottles using a single backpropagation neural network. Here the worst case error was 3.38 percent.
Keywords: acoustic emission, amplitude distribution, backpropagation, burst pressure prediction,
composites, filament wound, graphite/epoxy, neural networks, nondestructive evaluation, pressure vessel.
Introduction
Acoustic Emission
Acoustic emission (AE) is a nondestructive evaluation method that involves instrumenting a
specimen with piezoelectric transducers and recording parametric representations of the
wave form data from flaw growth activity in order to perform a structural integrity analysis.
Analysis Of the AE data allows for the determination of failure mechanisms that are active in the
specimen. Consequently, it also contains information concerning the structural integrity.
Burst Pressure Prediction
The prediction of burst pressures in both damaged and undamaged filament wound
composite pressure vessels has been previously accomplished using linear multivariate statistical
analysis and backpropagation neural networks [1-3]. The goal of this research was to utilize a
backpropagation neural network to make burst pressure predictions on 15 inch (380 ram)
diameter graphite/epoxy filament wound composite overwrapped pressure vessels (COPVs
otherwise known as bottles) that were varied in the method of cure, type of damage, temperature,
and pressurization scheme: What made this research different from its predecessors was that the
disbonding of the composite overwrap from the aluminum liner generated multiple hit AE data
(noise) which had nothing to do with the structural integrity of the vessels. This precluded a
straightforward solution similar to those obtained previously until the noise data were eliminated.
Neural Networks
Artificial neural networks are a diverse set of robust mathematicai toois used to ciassify data
into clusters, recognize patterns, process signals, and do predictive modeling and forecasting.
Here an unsupervised SOM neural network was used to classify the composite failure
mechanisms that occur during pressurization. The backpropagation architecture is a feed-
https://ntrs.nasa.gov/search.jsp?R=20080013318 2019-08-30T04:11:01+00:00Z
forward design that was subsequentlyemployed for making supervised burst pressure
predictions.
Kohonen Self Organizing MaD (SOM)
In composite structures, the amp!itude frequencies [of occurrence] generated during damage
progression can be grouped and classified into failure mechanisms. For small data sets these
mechanisms can be seen as "humps" in the AE amplitude distributions. Figure 1 shows the
amplitude distribution for bottle SN002, an impact damaged graphite/epoxy COPV used for
training the backpropagation network, which appears to have four humps. This number of
failure mechanisms (four) was confirmed by classifying the AE amplitude data with a Kohonen
self-organizing map (SOM) neural network.
AE Amplitude Distribution (SN002)
C_
350
2 o1!!!\
2°°11,IIt,\l/II!J! \
60 70 8° 90 100
AE Amplitude [dB]
Figure 1 AE amplitude distribution for COPV SN002
Baekpropagation
The backpropagation neural network is a feed-forward, supervised neural network, that is to
say, it does not return a feedback signal to itself during each training pass, and it is necessary to
train the network on a known solution before applying it to a new case. This is called supervised
learning. A backpropagation neural network is typically constructed with an input layer, one or
more hidden layers (each composed of multiple neurons) for mapping, and an output layer.
Experimental Procedure
Pressure Vessels
Eleven 15-inch diameter filament wound COPVs were wet wound on a filament winder. The
bottles were thin-walled aluminum cylinders overwrapped with graphite fibers and epoxy resin.
The winding sequence was 3 inner hoop plies, followed by 2 helical layers, and then 2 outer
hoop plies. Eight of the bottles were rotated at slow speeds (rotisserie style) during oven curing;
three were oven cured without rotation. Four of the COPVs were tested at ambient temperature,
while the remaining seven experienced cryogenic temperatures. Due to the nature of
piezoelectric materials, it was thought that the large variation in temperatures would have a
significant effect on transducer output voltage, as well as adding to the brittle nature of the
composite material.
Varying amounts of artificial damage were inflicted on the bottles in the form of impacts
from both blunt and sharp tups, as well as with the cutting of hoop fibers: five tows were cut in
the mid hoop ply and five in the first outer hoop ply. The strain rate was also varied non-
systematically in that the pressurization scheme used Oil each bottle test varied in both duration
and number of pressurization ramps/holds; thus, no two bottles were pressurized alike.
The amount of diversity in some of these variables and the small number of bottles would not
allow for statistical analysis of the effects of each variable. Therefore, neural networks were
em_ployedasthepri_m__arymethodof dataanalysis.
with thefailureor burstpressures.
Table1 summarizesthe test variablesalong
Table 1 Summary ofgra
SIN
002
003
005
009
010
013
014
018
020
025
026
Damage
Impacted
Impacted
None
phite/epoxy COPV variables and burst pressures
Cure Type
Static
Rotisserie
Rotisserie
StaticNone
None Static
None
None
Impacted
Lacerated
Rotisserie
Rotisserie
Rotisserie
Test
Temperature
Cryogenic
Cryogenic
Ambient
Cryogenic
Cryogenic
Ambient
Cryogenic
Ambient
Burst Pressure
[psig]
1880
2004
2960
2544
2460
2874
2390
Lacerated 2864
Rotisserie Cryogenic 1967
Rotisserie 2393
Rotisserie
Cryogenic
Ambient 2675Lacerated
AE Data Collection
Acoustic emission data were successfully collected from all seven of the bottles in the test
set. A multi-channelPhysical Acoustics Corporation (PAC) AE analyzer was used to record the
acoustic emission flaw growth data from seven AE channels, each representing a transducer at a
unique location on the test bottles. This data acquisition unit also allowed for a separate
parametric input, which was used to record a +5.0 volt signal representative of the pressure in the
test specimen. Figure 2 shows a schematic diagram of the test setup.
Six PAC AE transducers were mounted equidistant around the circm'rderence of the top mad
bottom hoop winds on each bottle. The seventh transducer was mounted near the upper polar
boss on the helically wound portion of the bottles. In the ambient temperature tests, hot melt
glue was used to bond each transducer and provide acoustical coupling between the transducer
and the specimen. For the cryogenic tests, high-aqueous vacuum grease and a mechanical
housing were used to couple the transducers to the bottles. The data sampling threshold was set
to record all acoustic emission hits that had an amplitude of 60 dB or greater.
PRESSuRTOIz'ATION t _-3
SYSTEM_ESSURE SENSOR
7 iv:5 s(}
POLAR B_
TO COMPUTER
Figure 2 Sensor positioning and test setup
AE Data Fihering
Even though constraints such as amplitude threshold, peak definition time (PDT=100/zs), hit
definition time (HDT=500/_s), and hit lockout time (HLT=500 #s) were applied to the acoustic
emission sampling, considerable noise and mult_le hit data were still present in the raw data
sets. HDT is the minimum time that an acoustic emission event must have to be recorded, and
the combination of HDT and HLT determines the maximum time for an event before it is
considered to be a multiple hit event [4]. These settings work in real-time as data are recorded.
Multiple hit data occur when many acoustic emission waveforms reach the transducer closely
spaced in time, one after another (in a condition of buffer overrun or in the cases where HDT and
HL]7 are not properly set). This results in a long artificial waveform that is actually comprised of
several smaller waveforms, which will have vastly different AE parameters than single hit data.
In this case, the multiple hit data are probably the result of composite disbonding fi:om the
aluminum liner, a failure mechanism which should not affect :the burst pressure.
In order to remove these multiple hits from the recorded data set, it was determined that any
acoustic emission hits having durations longer than 100 ms were to be removed. Rise time, the
waveform parameter that represents the time-to-peak of the waveform, was used to further
remove suspected multiple hit data. Long rise times typically indicate multiple hits logged
together. Thus, any hit with a rise time of greater than 25 ms was also removed from the data
set. The AE energy parameter is a measure of the area under the rectified waveform envelope.
In the case of these data, many hits were reported by the data acquisition system to have zero
energy. These hits were also discarded under the assumption that they were noise.
The final filter applied to the data was to select those data points to be used in the actual burst
pressure prediction. After removing all of the data prior to the start of pressurization (the
acquisition hardware recorded AE parameter data before pressurization commenced), it was
decided that the first 2000 data points would provide a sufficient sample Size for the neural
network to train on while still taking only those data that were acquired at or below 20 percent of
the anticipated burst pressure. In general, damage is inflicted on composite bottles during any
pressurization cycle; therefore, the goal was to predict on data taken at low proof pressures.
Using the final edited data set _om each bottle, a t2equency distribution of the amplitudes
was extracted for training and testing of the backpropagation neural network. The histogram
representation of an amplitude distribution for bottle S/N 002 can be seen in Figure 1, and the
amplitude distributions from the edited data for alJ eleven bottles are summarized in Table 2.
The neural network was trained to analyze the subtle differences in the distributions 12om each
bottle and match them to the damage type and burst pressures provided in the training set.
Table 2 Finalized amplitude distribution frequencies (with categorical variables in bold)
S/N
002
003
OO5
009
Amplitude Distribution Frequency Data
0 1 0 2 11 177 319 289212 171 139 109 87 69 70 63 50
292930192416713686365531431310320
7O
Burst
Pressure
[psi_]
1880
0 1 0 2 66 452 46I 268 171 130 89 76 44 37 40 25 35 20 2004
13 17114107241242000102000000141
0 0 1 1 169 148 121 115 95 I23 88 8i 78 62 59 74 53 53 i
586147 46525838484334293128212121 1077 2960
46703100
2544
[ 2460
0011393604522851771198882704642314028
322013988646773143302000100040
_ _ t fl_l 1,17Rg740f)),0£ 10411411275585855344432
010
2920-19i088"51-08411262110110111110
I 0 0 1 1 71401 380254 169 96 76 67 61 42 44 4942 29
013 233316 1616211515129741087303001000
000
2874
--7
tal
018
020
025
026
0 01 1 67 477 475 309 193 98 66 54 29 47 22 25 24 13 2
10910 74944123134053111010213
2390
1 0 0 3 225 222 211 176160 I36 137 99 98 80 85 67 54
244402319211515526420001000001000 2864
00
0 1 0 16 212 369 301 230 179 147 99 95 65 47 42 30 35 1967
2512128 13710540517423120 I2000054
1 0 0 3 30 224 309 294216 176 132 102 99 76 63 39 48
362619 181512101386503001025011110 2393
072
2675
1 0 0 3 i3 108 166 199 199.205 t73 167 135 103 107 86
665847 292935 15 16588513233101 11010
0010
Results
Network Architecture
The backpropagation neural network used herein [4] had the architecture shown in Figure 3.
The input for each bottle consisted of a 1 x 44 dimensional vector with 3 entries representing the
damage categories (001 undamaged; 010 impacted; and 100 lacerated) plus 41 integers
representing the frequency distribution of amplitudes from 60 dB to 100 dB (Table 2). The
actual burst pressure was also supplied as an input for error calculation at the output.
Each neuron in the hidden layer contains a hyperbolic tangent activation or transfer function
that can be used to approximate the shape of the amplitude distributions. A large number of
neurons can be used together to approximate compound and/or discontinuous curves that will fit
the training data well, but if trained too closely, the backpropagation neural network may not
predict accurately on the test data. Too few neurons in the hidden layer will result in loosely- fit
curves that will not correspond weii to the training or test data. Using this approach, it was
found that 11 neurons in the hidden layer offered the network that would best fit both the training
and the test data.
Damage 2
Type
Variat_les
(3 Neurons)
t Layer
Amplitude (Burst Pressure-
Histogram 1 Neuron)
(41 Neurons)
Hidden Layer
{11 Neurons)
Input Layer
(44.._,.rmns)
Figure 3 Network architecture
Training the Network
The backpropagation neural network, generated using Neuratware's NeuralWorks
Professional II Plus software package, was trained on a total of seven COPVs, including bottles
from each of the three damage "_'_-_'_o Cq-o_,l_ 3). T_,_ " _.... t chosen so that a bAgh
and low burst pressure COPV from each damage category was included. The undamaged
oate_ar'xr nl_c_ inolnctart n mictrnna_ hnr_t nre_qllra hattle Thn_ the enaeh ,qize wag _even nr the
total number of bottles in the training set.
After numerous experimental iterations, the optimum network architecture and input
parameters were determined [4]. The learning tale was the normalized cumulative delta, and the
optimal learning coefficient for the network was found to be 0.30 with a momentum of 0.40.
Thisrelativelylargelearningcoefficientallowedthenetworkto trainveryquickly andto learnin
only 71 cycles. Becausethe network trainedso quickly, bias neuronswere not employedto
speedup the training process. Transition points and learningcoefficient ratios were not
importanteitherbecausethedefaulttra_n_sitionpoint of 5000cycleswasneverreached,while the
F' offsetwassetat 0.10.
A root meansquare(RMS) error valueon the sevenCOPVsin the trainingset is computed
by the soft-wareafter every trainingcycle to determinehow well thenetwork hastrained. Here
theRMS error convergencewasset at sevenpercent. This meansthat trainingwas considered
completewhen the network training curvewas within anRMS error of sevenpercentof the
trainingdata. It was foundthat ahigherconvergencecriterionleft thenetworktoo looselyfit to
thetraining.data,anda lowerconvergencecriterion forcedtighter fitting of the trainingdatabut
poorerfitting of thetestdata.
Burst Pressure Predictions
Finally, the trained network was used to predict burst pressures for both the training and test
sets. Table 3 shows a summary of the prediction results on all the COPVs. The maximum
prediction error in the seven bottle training set was -2.78 percent, and the maximum error in the
four bottle test set was 3.38 percent. All of these values were well within the goal of predicting
the burst pressures to within a _-5 percent error.
Table 3 Summary of training and test results
I Predicted
I Burst Burst
Pressure Pressure Error
S/N Damage Purpose [psig] [psig] [%]
002 : _- ,Train: . ' _3880 i i&827,597,, -2.7,8
003 ..... Train: 2004 ,: t5., - -I,99::_
005 None Test 2760 2853.188 3.38
009, ::; :None -!" ':Z/_{2544 : ": 2584:266 .Q,1.5:8::,
010 None Test 2460 2432.273 - i. 12
013 None _" = Tr_i{h:,. _:; _2874- _ =2791,009, =_ -,2.88- :
-014 _Nbne.:[" Traiifi/_ )2390 _:2:35.8:274:_,'. :1232 :
_7018 - .,,Lacerated Tmili, :, 2864- _,:2869.=18-i (', _.0,18/:
020 Impacted Test 1967 1999.875 1.67
025 - Lacerated " Train ..... ,: 2393 / /)2369.740?: -0:97 ?
026 , Lacerated Test 2675 2643.174 -1.19
Conclusions
The worst case prediction error of 3.38 percent was very low, and the network trained
quickly in spite of the many test variables involved. If there were any variations in the amplitude
distribution data due to cure mode, temperature, and pressurization scheme, they were
automatically taken into account by the backpropagation neural network. The fact that network
training was accomplished in only 71 cycles attests to the effectiveness of preprocessing or
editing the AE data to remove the multiple hit data and other noises:
References
1. E.v.K. Hill, J.L. Walker II, and G.H. RoweR, Materials Evaluation, 54, 1996, pp. 744-748.
2. J.L. Walker, S.S. Russell, G.L. Workman, and E.v.K. Hill, Materials Evaluation, 55, 1997,
pp. 903-907.
3. M.E. Fisher and E.v.K. Hill, Matenais Evaluation, 56, i_, pp. i395-i40i.
4. S.-A. T. Dion, MSAE Thesis, Embry-Riddle Aeronautical U., Daytona Beach, 2006.


Neural Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels

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