r. a. bailey/ r. m. pidaparti,* m. j. palakal engineering & … · 2014. 5. 15. · of 505 mpa,...

Corrosion prediction in aging aircraft

materials

R. A. Bailey/" R. M. Pidaparti,*" M. J. Palakal

Engineering & Technology?, IUPUI, Indianapolis, IN 46202, USA

^Department of Computer Science, Purdue School of Science,

Abstract

An artificial neural network is developed to predict the corrosion behavior ofdifferent series of aluminum alloys when exposed to a variety of corrosivesubstances, short term and long term aircraft carrier exposures. Given thecorrosion environment and time of exposure the neural network predicts theASTM G34 corrosion rating and the resulting material loss. The trained andlimited test results predicted from the neural network are in good comparison tothe experimental data. The effects of corrosion environment and material typefrom neural network simulation are presented to illustrate the trends. Based onthe preliminary results, the neural network approach to corrosion predictions isencouraging and can be used for a variety of materials and environments if moredata is available. It is possible to use another neural network to predict therequired exposure time to produce a particular corrosion classification in anenvironment. It is intended that the approach developed here will assist in thestructural integrity evaluation of aging aircraft.

1 Introduction

The civilian and military air fleets in the United States are flying longer withoutreplacement than anticipated when they were originally designed. Concernsabout the structural integrity of these aircraft are being addressed from manyperspectives. A major cause for concern is corrosion, among others likewidespread fatigue damage. The primary approach to suspected structuraldegradation of aircraft has been non-destructive examination and evaluation of

Damage & Fracture Mechanics VI, C.A. Brebbia, A.P.S. Selvadurai, (Editors) © 2000 WIT Press, www.witpress.com, ISBN 1-85312-812-0

588 Damage and Fracture Mechanics VI

the damage, and subsequent prediction of residual strength and fatigue life.Corrosion exacerbates the effects of stress and fatigue not only when corrosionoccurs simultaneously but also as a result of pre-existing corrosion [1].Scheming and Grandt [2] have shown that fatigue life and fatigue crack growthrate of aircraft aluminum are adversely effected by pre-existing corrosion.

Current research on pre-existing corrosion such as that done by Luzar at Boeing[3] aims to predict the adverse effects of pre-existing corrosion. A different butalso very important goal is to develop a corrosion prediction model. Tritsch andKonish [4] have used statistical models and a model based on corrosion damagemechanics to try to predict corrosion behavior. The ability to predict whensignificant degradation of aircraft structure will occur is crucial to controllingthe cost of maintenance by indicating which aircraft need to be scrutinized fordamage. Designers today are still using the same materials that are on today'saging aircraft. It is important that they have foreknowledge of the corrosionthose aircraft will experience in years to come.

The characterization of the corrosion behavior in aircraft materials is verycomplex. It is also very difficult to quantitatively evaluate the extent or natureof corrosion damage. In addition to those problems, aircraft materials areintended to be corrosion resistant. While that is good for the aircraft, it createsdifficulty because tests that are done in even the harshest aircraft environmentscan take several years to produce a specimen with significant corrosion.Correlation between accelerated corrosion tests and the actual environmentsexperienced by aircraft are almost completely qualitative. These problems indeveloping a corrosion model are the sort that are ideally suited to the use of aneural network approach.

The objective of this study is to develop a neural network model that willpredict the corrosion rating based upon ASTM G34 classifications given thematerial type, thickness, and environment. Simulation results are obtained toillustrate the trends due to material type and environment.

2 Approach

Neural networks are a neurobiological analogy of the brain. They obtainknowledge of a process by training with sample input data and known outputdata. A back-propagation neural network is used in this study. It is one ofseveral different kinds of neural network and has been used successfully topredict behavior of engineering problems including corrosion of carbon steel

[5].

The neural network is composed of a number of artificial neurons. Eachartificial neuron receives information, usually from several sources, sums the


Damage and Fracture Mechanics VI 589

inputs, and uses a transfer function to produce an output [6]. A schematicrepresentation of the neural network model is presented in Figure 1. The inputlayer neurons, which are the analogue of sensory units, receive the system input.The hidden layers contain neurons that are computational nodes. The outputlayer also has neurons that are computational nodes and the system output dataare produced here. The inputs to each neuron of the first hidden layer comefrom the output of all the input layer neurons. The next layer of neurons receiveinputs from the output of all the previous layer nodes and so on throughout thenetwork, (see Figure 1) Each connection between neurons has an individualweight. The summation process is a weighted sum based on the connectionweights. The output of the neuron is then the sigmoid function of the weighted

During training a global error function is calculated from the output layerresults and the actual results. This allows the calculation of local error at eachnode by back-propagation of the error. The weight of each connection in thenetwork is then revised. The training process continues iteratively with the goalof minimizing the global error between specified output and the networkprediction. Pidaparti and Palakal used neural network methods to predict fatiguecrack growth of aircraft panels under arbitrary spectrum loadings [7] andmultiple site damage aircraft panels [8].

The neural network developed for this study uses 13 input layer nodes, has twohidden layers with 32 and 16 nodes respectively, and has 2 nodes in the outputlayer as shown in Figure 1. The input parameters are: thickness, aluminumseries material designators, yield strength, environment designators,temperature, and duration of exposure. The two output parameters were ASTMG34 corrosion rating, and cm/cm"-days material loss rate. It is important to notethat this is thickness lost per unit area per elapsed days of duration. The secondoutput was originally chosen to be cm/crrr of material lost but was changed bydividing by days of exposure because the range of values for material lost wasseveral orders of magnitude in size which caused training problems. A learningcoefficient of 0.9 and a momentum term of 0.7 were used in training the neuralnetwork.

A typical input/output data set for the neural network model is presented inTable 1. The materials that the network is made to accept as input are 2XXX,3XXX, 6XXX, and 7XXX series aluminum alloys. The environmentsconsidered include, saturated Hydrogen Sulfide, saturated Carbon Dioxide,saturated Sulfur Dioxide, modified ASTM B368 acetic acid salt fog test with pHof 2.5, and aircraft carrier deck exposure. All the environments are gaseous,and were selected for their relevance to actual aircraft working environments.The ASTM G34 corrosion rating is presented in Table 2.



The material designators are mutually exclusive as are the environmentdesignators. Thus if a 1 is entered for the 2xxx series of materials all the othermaterial designators are zero. The ASTM G34 corrosion ratings [9] wereconverted to numerical data as in Table 1.

3 Results and Discussion

The data for training and testing of the neural network was collected from thedifferent sources including; Boeing [3], JOM [11], and "Corrosion ResistantMaterials Handbook" [12]. A total of 15 data sets were used for training and 2for testing. During training the network converged after less than 150,000iterations. The trained results from the neural network presented in Figures 2and 3 show training predictions versus experimental data. Perfect training wouldbe exactly on a 45 degree line. These indicate that the network trained verywell. However results from data sets that were tested showed that the networkwas not able to generalize the process as well, especially for material loss rate.See Table 3. Several hundred data sets would have been desirable to train theneural network.

Four simulations were run using the neural network to predict results. Theduration of the simulation was varied in order to produce trend data.Simulation # 1 was for material 2.54 cm thick, 7XXX aluminum, yield strengthof 505 Mpa, temperature 29.5 C, and aircraft carrier environment.Simulation # 2 was for material 2.54 cm thick, 7XXX aluminum, yield strengthof 505 Mpa, temperature 29.5 C, and modified ASTM B368 environment.Simulation # 3 was for material 2.54 cm thick, 2XXX aluminum, yield strengthof 310 Mpa, temperature 29.5 C, and aircraft carrier environment.Simulation # 4 was for material 2.54 cm thick, 2XXX aluminum, yield strengthof 505 Mpa, temperature 29.5 C, and modified ASTM B368 environment.

The results of the neural network simulations are shown in figures (4-11). Allof the trends are linear for the thickness of material used in the simulations. Theoutput # 1 results show corrosion rating increasing with time as it should.However the lines that were fit to the data do not go exactly through the originas would be expected for zero corrosion for zero days of exposure. They alsocorrectly indicate greater corrosion for the accelerated test vs. the aircraft carrierenvironment. For the simulated aircraft carrier environment the ratio ofcorrosion ratings for 7XXX divided by corrosion rating for 2XXX material is3.4. The same ratio for the modified ASTM B368 environment is 0.634. Thisindicates that there is a fundamental difference in the behavior of these materialsin the aircraft carrier environment as compared to the accelerated testenvironment.



4 Conclusions

An artificial neural network was developed to predict the corrosion behavior ofdifferent aluminum alloys when exposed to a variety of environments. Thetrained results are in good agreement with the experimental data. The limitedtest cases are in reasonable agreement with the experimental data. The networkdid not generalize for the test cases with great accuracy due to lack of data.However, the speed with which the network converged indicates that all that isneeded is a larger set of training data. The simulations indicate the practicalityof using the neural network predictions to study the effect of various corrosiveenvironments of different aluminum alloys. Given a larger and more diverse setof training data, simulations could be run for many different combinations ofmaterial, thickness, and environment. The approach presented will be helpful tothose managing aging aircraft fleets. It would also be valuable to designers inthe choice of material type, thickness, and as a guide for correlation betweenaccelerated tests and the real world.

Acknowledgment

The authors thank the U. S. National Science Foundation for supporting thiswork through a grant CMS-981273. Thanks also to Mr. Joseph Luzar of Boeingfor providing information.

References

1. Koch, Gerhardus H., "On The Mechanisms of Interaction BetweenCorrosion And Fatigue Cracking In Aircraft Aluminum Alloys", StructuralIntegrity of Aging Aircraft, edited by C. I. Chang and C. T. Sun, AD-Vol.47, American Society of Mechanical Engineers, 1995, pp. 159-169.

2. Scheming, Jason N., and Grandt, Alten F. Jr., "An Evaluation of AgingAircraft Material Properties", Structural Integrity of Aging Aircraft, editedby C. I. Chang and C. T. Sun, AD-Vol. 47, American Society ofMechanical Engineers, 1995, pp.99-110.

3. Luzar, Joseph, "Pre-Corroded Fastener Hole Multiple Site DamageTesting", EA 96-135OTH-041, /(C-/JJ FWf &//;/,orf CW/7Y7cf, F34601-96-C-0111, Tuesday, September 22, 1998, pp. 1-46.

4. Tntsch, Doug E., and Konish, H. J., "Maintainability ImprovementThrough Corrosion Prediction", The Second Joint NASA/FAA/DoDConference On Aging Aircraft, Williamsburg, Virginia USA, 1998.


592 Damage and Fracture Meehanies VI

5. Nesic, Srdjan, and Vrhovac, Milan, "A Neural Network Model forCorrosion of Carbon Steel", The Journal of Corrosion Science andEngineering, Volume 1, Paper 6, Submitted 6 February 1997, pp. 1-14.

6. Hay kin, Simon, "Neural Networks A Comprehensive Foundation",Macmillan College Publishing Company Inc. 1994.

7. Pidaparti, R. M. V., and Palakal, M. J., "Neural Network Approach toFatigue-Crack-Growth Predictions Under Aircraft Spectrum Loadings",

/\#rm/? Vol. 32, No. 4, July- August 1995, pp.825-83 1.

8. Pidaparti, R. M. V., and Palakal, M. J., "Fatigue Crack Growth Predictionsin Aging Aircraft Panels Using Optimization Neural Network", AIAAVoz//v7^/ Vol. 36, No. 7, July 1998, pp. 1300-1304.

9. ASTM B368 - 85 (Reapproved 1990), "Standard Method for Copper-Accelerated Acetic Acid-Salt Spray (Fog) Testing (CASS Test)".

10. ASTM G34-90, "Standard Test for Exfoliation Corrosion Susceptibility in2XXX and 7XXX Series Aluminum Alloys (EXCO Test)".

11. Tankins, E., Kozol, J., Lee, E. W., "The Shipboard Exposure Testing ofAircraft Materials", JOM September 1995, Vol. 9, pp.40-44.

12. DeRenzo, D. J., "Corrosion Resistant Materials Flandbook" Fourth Edition,Noyes Data Corporation 1985, pp. 609.



Table 1 Typical Input/ Output Data for Neural Network Model

Input data

Thickness(cm)

2xxx series

3xxx series

6xxx series

7xxx series

Yield strength (MPa)

Hydrogen Sulfide H^O sat.

Carbon Dioxide H2OSat.

Sulfur Dioxide h^O sat.

Aircraft Carrier

ASTM B386 (acetic acid

salt fog)

Temperature (°C)

Duration of Exposure

(days)

[3]

0.1575

1

0

0

0

310

0

0

0

0

1

49

30

Output data [3]

Corrosion rating 1

Rate (cm/cnf - 0.00155

days)

Table 2 Explanation of ASTM Corrosion Ratings used inthe Neural Network

Rating

N

P

EA

EB

EC

ED

Description

No appreciable attack

Pitting

Superficial exfoliation

Moderate exfoliation

Severe exfoliation

Very severe exfoliation

Range

0

0-0.2

0.2-0.4

0.4-0.6

0.6-0.8

0.8-1.0

Table 3. Corrosion Rating and Material Loss Rate Predicted forTest Data

Test 1

Test 2

Corrosion

Rating

0.003

0*

1

1*

Material Loss

Rate

0.0018

0.00037*

0.2585

0.03874*

indicates actual experimental value



CorrosionParameteis

INPUT

iVkenalLossRate

ourrur

Figure 1: A Schematic of the Neural Network Model for

Corrosion Prediction

Corrosion Rating

0.2 0.4 0.6 0.8Experime nta| Data

NN Predicted Linear (NN Predicted)

1.2

Figure 2: Comparison of Neural Network Prediction with

Experimental Data for Corrosion Rating


Damaee and Fracture Mechanics VI 595

Corrosion rate

0.4 0.6Experimental Data

0.8

NN Predicted • -Linear (NN Predicted)

Figure 3: Comparison of Neural Network Prediction with

Experimental Data for Corrosion Rate (cm/cm2-days)

Simulation # 1Output # 1

0.4 , - - —

0.35

% 0.350,0.25c

0.2

0.15

0.1

0.05Ji

0 \-

y = 0.0002x + 0.249

0 100 200 600 700300 400 500

Duration (days)

• output #1 Linear (output #1)

Figure 4: Simulation results of corrosion rating for 7XXX

material under aircraft carrier environment

800



0.0014 r

_ 0.0012

0001

"g 0.0008

0TO 0.0006c•§ 0.0004

Q 0.0002

0

Simulation # 1output # 2

y = -5E-07x + 0.0012

100 200 300 400 500 600

Duration (days)

• output#2 Linear (output#2)

700 800 i

Figure 5: Simulation results of corrosion rate for 7XXX material

under aircraft carrier environment


Corrosion R

ating

0.999455

0.99945

0.999445

0.99944

0.9994350.999430.999425

0.999420.999415

0.99941

0.999405

y = 7E-07X + 0.9994

10 20 30 40Duration (days)

50

output#1 -Linear (output#1)

60 70


material under ASTM B368 environment




0.001603

00016025

-g 0.001602

0.0016015

oO

0.001601

0.001600510 20

output#2

30 40 50

Duration (days)

— Linear (output#2)

60 70


under ASTM B368 environment

Simulation #3Output # 1

100 200 300 400 500 600

Duration (days)output #1 Linear (output #1)

700 800


material under aircraft carrier environment



0.0009

^ 0.0008(/)^ 0.0007"91 0.0006

I 0.0005

•Sj 0.0004

c 0.0003

| 0.0002oO 0.0001

0


y = -6E-07x + 0.0008

100 200 300 400 500 600 700

Duration (days)

• output#2 Linear (output#2)

800


under aircraft carrier environment


10 20 30 40 50Duration (days)

output #1 Linear (output #1)

60 70


material under ASTM B368 environment



0.001385

0.00136 --0


10 20 30 40

Duration (days)

50 60 70

$ output#2 Linear (output#2)


under ASTM B368 environment


r. a. bailey/ r. m. pidaparti,* m. j. palakal engineering & … · 2014. 5. 15. · of 505 mpa,...

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