NASA/TM-- 1998-208814

Comparison of Computational-Model

and Experimental-Example Trained

Neural Networks for Processing

Speckled Fringe Patterns

A.J. Decker, E.B. Fire, S.A. Thorp, and O. Mehmed

Lewis Research Center, Cleveland, Ohio

November 1998

https://ntrs.nasa.gov/search.jsp?R=19990009742 2018-05-11T16:58:01+00:00Z
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NASA/TM-- 1998-208814

Comparison of Computational-Model

and Experimental-Example Trained

Neural Networks for Processing

Speckled Fringe Patterns

A.J. Decker, E.B. Fite, S.A. Thorp, and O. Mehmed

Lewis Research Center, Cleveland, Ohio

Prepared for the International Conference on Optical Technology

and Image Processing in Fluid, Thermal and Combustion Flow

cosponsored by the Visualization Society of Japan

and SPIE - The International Society of Optical Engineering

Yokohama, Japan, December 7-9, 1998

National Aeronautics and

Space Administration

Lewis Research Center

November 1998
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Proceedings of VSJ-SPIE98

December _9, 1998, Yokohama, Japan

AB002

Comparison of Computational-Model and

Experimental-Example Trained Neural

Networks for Processing Speckled

Fringe Patterns

Decker, A. J., Fite, E. B., Thorp, S. A., Mehmed, O.

NASA Lewis Research Center

Cleveland, Ohio 44135

USA

___d_ e:c_,_k_ _r_@__l_m_" :__a__a:go_:(

Abstract: Tile responses of artificial neural networks to experimental and model generated inputs are

compared for detection of damage in twisted fan blades using electronic holography. Tile training set

inputs, for this work, are experimentally generated cllaracteristic pattenls of tile vibrating blades. Tile

outputs are damage flag indicators or second derivatives of tile sensitivity vector projected displacement

vectors from a finite element model. Artificial neural networks have been trained in tile past with

computational model generated training sets. This approacll avoids tile difficult inverse calculations

traditionally used to compare interference fringes with tile models. But tile high modeling standards are

hard m acllieve, even with fan blade finite element models.

Keywords: Neural Networks, Interferomett2¢, Electronic Holography, Image Processing,

Speckle Metrology

1. hltroduction

There is a continunlg need to convert whole field data such as interference pattenls of flows and structures into

two and tluee dimensional distributions of properties. Tile traditional viewpoint is that tile instrument system should

convert tile raw optical pattenl to a two or tluee dimensional distribution of tile quantity of nlterest sucll as velocity,

density, displacement or strain. Tile instrument derived results are then compared ostensibly with predictions of

computational models or are used directly to operate test facilities. That viewpoint no doubt is most friendly to tile users

of optical measurement instrumentation, but suffers from some serious practical and scientific defects. Tile conversion of

interference pattenls into densities of flows or strain fields of structures, for example, is generally ill posed and requires

often arbitrary regularization procedures. Tile measurement process invariably contains some untested assumptions: one

classical example is tile assumption in laser anemometry or particle image velocimetry that seed particles track flows. A

worknlg nlstrument may not be well matched to tile environmental conditions or operating conditions required by a user.

Fnlally, a whole field optical measurement system may not have tile processing speed to meet real time requirements.

An altenlative viewpoint of whole field data conversion has been under test at NASA Lewis Research Center for

a number of years. Tile concept is to compute all optical pattenl by combining a phenomenological model of a flow or

structure with a model of tile optical measurement process. Measured pattenls are then compared with tile computed

pattenls to identify a best match between tile measured and model generated flow or structural conditions, or whether a

matcll even exists. This approach avoids ill posed inverse calculations. Tile ultimate user or customer provides tile

phenomenological models and is directly involved in determining whether tile instrument will meet customer

requirements. This approach can be added to existing optical measurement systems, but it requires a fast interface

between tile computational model generated pattenls and tile measured pattenls.

Artificial neural networks, particularly tile feedforward neural network, are being tested at Lewis as fast

interfaces between computational model generated fringe pattenls and measured fringe pattenls. Our initial application of

tile neural network interface was to computed tomography (Decker, 1993). Neural networks have been used during tile

past two years to process tile time average cllaracteristic pattenls computed from electronic holograms of vibrating

structures. Neural net processing of these holograms constitutes a complete test of tile concept of usnlg a neural net

interface for flows or structures. Finite element models of vibrating fan blades are tile phenomenological models. An

optical model of electronic holography, complete with teclmiques for handling tile laser speckle effect, has been developed

(Decker et al., July 1997). There is a strong customer interest in tile teclmique and a willingness to participate in

developing tile neural net interface since there is a need to display strain or damage information for fan blades. Finally,

neural net processing of cllaracteristic pattenls has been demonstrated at up to 30 frames per second (Decker et al., May

1998), thereby providnlg a fast interface.

Tile following question has surfaced during this work with neural net interfaces: Do tile finite element models

normally used to design fan blades generate sufficiently accurate pattenls to train tile neural networks? A major reason for

testing tile neural net concept with structural models was tile assumption that structural models would be better known

than flow models. As mentioned in tile references above, tile assumption proved correct for cantilever plates. But recent

work with twisted blades has indicated that normal design models may not provide enough accuram detail to train tile

NASA/TM--1998-208814 1
neural netinterface, particularlyatfileresolutions requiredfordamagedetection. Neural netshaveproventobevery

exactingforcomparing model generated patternswithmeasured patterns.

Thispaperis a comparisonf neural net processingof experimental examplewithmodelgenerated

characteristic patternsoftwistedfanblades.Thebladeswereconstructed specifically forthesetests,buthavevibration

propertiessimilartotheproperties ofbladesinbladeddisks(blisks).Tlueebladeswereconstructed, andonebladewas

intentionally crackedtluoughshaker inducedhighcyclefatiguefailure.Thecharacteristic patternsweregenerated using

twoframelectronictimeaverageholography, wherethephaseofthereferencebamwasshiftedby180degreesbetween

alternateframes.Subtraction oftheframesthenyieldedhighcontrast characteristic patterns.

2. Neural NetTrainingRecords

Thearchitecture oftheartificial neural networksandthecomposition ofthetrainingrecordshavebeendiscussed

indetailpreviously(Deckertal.,July1997),(Deckertal.,May1998).Thecomputer platforms, neural netsoftware

andvideowerealsodiscussed. Bothpublications treatedcantilever platesasexamples; whereasthispaperteatsatwisted

bladeasanexample. Thepreviouspublications dicussedmodel trainedartificialneuralnetworksexclusively; whereas

thispaperdiscusses bothmodelandexperimental exampletrainedneuralnets.Thefollowingdiscussion reviewsthe

neural netsandtrainingrecordsbrieflyandintroduces somenewfeatures.

SoftwarecreatedatNASALewisResearchCenterisusedtogeneratethetrainingrecordsandtrainingsets

automatically. Thissoftware, together withthepackageusedtocreatehefeedforward neural networksandthevideoused

torecordtheholograms, areresident inaSGI02workstation. Thecombination hasbeendemonstrated atupto30frames

persecondforneural netprocessing ofcharacteristic patterns.

Thetrainingsetsrelevant tothispapercontainafewhundredtrainingrecords.Eachtrainingrecordcontainsan

inputvectorandanoutputvector.Theinputsarenormalized intherange0to1,andtheoutputsarenormalized between

0.2and0.8forthesigmoid(logistic)transferfunctionsoftheneural networks. Otherneural netarchitectures havebeen

trained. Butthecompacmess andnoisehandlingabilityofthefeedforward netmakesit definitelysuperior forprocessing

thespeckledcharacteristic patternsfromtimeaverageholography.

INSERT

(a) (b)

Fig. 1. Characteristic or time-average patterns of first mode of twisted Made: (a) at CCD-camera

resolution, where the insert shows the region measured for crack detection, (b) at finite-dement

resolution.

The input vectors always are finite element resolution dlaracteristic fringe patterns wifll file scan lines packed in

order. The design grade finite element models and workstations used to design compressor blades handle a maximum of

about 5000 elements. Hence, the input vectors will contain a few hundred to a few thousand pixel values. Figure 1 shows

both CCD and finite element resolution characteristic patterns (not at the same vibration amplitude) for the first bending

mode of the twisted blade used as an example in this paper. The pixel sizes in the finite element resolution pattern were

calculated from the design model described in the next section. The input vector might contain a full blade pattern or a

pattern from a magnified small region of the blade. The insert in the CCD resolution pattern in fig. 1 was zoomed and

measured for crack detection, for example. The CCD cameras are always 640x480 pixel NTSC cameras.

As reported previously (Decker et al., July 1997), the neural networks can be taught to ignore the laser speckle

effect. To accomplish this objective, independent speckle patterns, equal in number to about 10 percent of the number of

linearly independent input vectors, must be presented during training. Hence, a training set composed of 1000 pixel inputs

would need approximately 100 linearly independent speckle patterns per characteristic pattern. Experimentally, sampling

NASA/TM--1998-208814 2
atrandomlocationswifllintimlargepixelsoftimfiniteelement resolutionarrayisusedtogeneratetimindependent

specklepatterns. Thisstepisnecessary sincethespecklepatternsremainhighlycorrelatedfromframetoframeoi1the

vibrationisolationtableusedtoconduct theexperiments. Theoretically, arandomnumbergenerator isusedtocreate

linearlyindependent specklepatternsfromthemodels(discussed inthenextsection).Samplinggridsendpixelsizesare

determined fromthefiniteelement modelsandaregenerallynotuniform.Figure2plotsthesamplinglocationsonthe

twistedbladesdiscussed inthispaper.

Fig.2. Finite-element-resolution samplingridfortwistedblades.

Theoutputvectorsareeiflmrmodel generated patternsordamageflags.Finiteelement model generated strain

patternscanbeusedastheoutput.Butthesecondspatialderivatives ofthesensitivityvectorprojecteddisplacement

amplitudevctorsofthevibratingobjectaremoreappropriate forelectronicholography. Thisclaimcanbeunderstood by

notingthathevisualizeddmracteristic patternisproportional to

(SpecklePattern) XJo(27tK"8)

whereK istimholographic sensitivityvector(Vest,1979)end_ istimdisplacement amplitudevctor.Thesecond

derivatives of K'5werediscovered toprovidesensitivedetectionfbladecracking,lmerestingly thebendinginduced

strainofaplatecanbecomputedfromthesecondderivatives ofthenormal components of _ inalocalcoordinate system.

Butholography visualizesaprojectionof _on Kratherthanthenormal.Thedisplacement disributions areobtained

fromthefiniteelement model.Figure3 showsdlordwisesecondderivativesof thesensitivityvectorprojected

displacements fromtheinsertinfig.1.Modelpredictedpatternsareshownforcrackedandundamaged twistedblades.

(a) (b)

Fig. 3. Model-predicted chord-wise second derivative of 1{5 of fig. 1: (a) for undamaged blade,

(b) for cracked blade.

These plots are slightly distorted by being plotted on a uniform grid. The output vector of tim training record will titan

contain a few hundred to a few thousand components. Sometimes, a simple indication or flag whether a blade is damaged

or undamaged is adequate. The output of the net, for example, can be a tluee component vector. One component indicates

a cracked blade; one component indicates an undamaged blade; and one component indicates that the pattern is not known.

We display this information by coloring the fringe patterns red, green and yellow, respectively. The damage flag can be

used with model generated or experimental samples.

The artificial neural networks then need to learn several hundred training records containing a few hundred to a

few thousand input nodes and possibly a few hundred to a few thousand output nodes. The optimization of the nets was

discussed in a previous publication (Decker et al., July 1997). The current results were obtained with single hidden layer

NASA/TM--1998-208814 3
feedforward neural networkscontainingfewhiddenlayernodes(sparsenets).About3 hiddenlayernodesarerequired

foreachcharacteristic pattern(noteachspecklepattern).Trainingtimeoi1theSGI02workstationwastypicallyafew

minutesfortheentiretrainingset.

Theeffectiveness ofacomputational model totestinterfacedependsoi1theaccuracyofthemodels.These

modelsarediscussed inthenextsection.

3. Models

31. Characteristic FrngePattern

Themodelsofthecharacteristic fringepatternhavebeendefinedinthereferences. Themodelsaredescribed

brieflyhere,andsomenewfeaturesarediscussed.

Thequantityprocessed bytheneural networksistheabsolutevalueofafiniteelement resolutionsamplingof

theexpression

(SpecklePattern) XJo(27tK'8).

Thatexpression wouldbegenerated experimentally bysubtracting two180degreephaseshiftedelectronicholograms. The

holograms areobtainedfromtwoadjacent framesorevenandoddfieldsofthevideo.Theworkdiscussed inthispaper

wasdonentirelywithframes.Evaluatingthedotproduct ofthesensitivityvectorK andthedisplacement _ requires

thepositionsoftheilluminationsourceandtheimaginglens,thegeometry andsamplinggridofthebladesurface, andthe

vectordisplacement field.Thelasttwoitemsaresuppliedwiththefiniteelement modeldescribedbelow.Thefinite

elements andsamplinggridgenerallyarenonuniform; hencethemodels, themeasurements andthedisplaymusthandle

nonuniformpixelsizes.Themodelhasbeensuppliedforthewholeblade.Zoomingona smallregionoftheblade

requiresinterpolationonthegenerallynonuniformgrid. Cubicinterpolationisusedfordisplacements andlinear

interpolationsusedforgridcoordinates, thecomponents ofthesensitivityvectorandstrains.

Therearemanypotential complications inmodelingtheimagesandspecklepatterns, includingnonuniform

illumination, aberrations, camerapixelsaturation, distortion, quantizationerror,pixelresponsevariationsandspeckle

statistics. Sofar,theneural netshaveproventobequiterobustinthepresenceofmostoftheseffects, andthespeckle

effecthasofferedthemajorchallenge. Specklestatistics, inprinciple, dependonthereferencetoobjectbeamratioand

thesurfacemicrostrucmre oftheblades(Goodman, 1975).Forsmallbeamratios,asimplemodel isadequate. The

intensityisdistributedaccordingtoanegativeexponential, andthephaseisuniformlydistributedin (0,2_) .Random

number generators selectthesequantifiesindependently frompixeltopixel.Infact,providedthatenoughindependent

specklepatternsareincludedinthetrainhlgset,variationsinthespecklestatisticsdonotseemtohavealargeeffecton

theperformance ofthenets(Deckertal.,July1997).

Themacroscopic structureofthecharacteristic fringepatterndependsprimarilyonthefiniteelement model

described brieflyinthenextsection.

32. FiniteElement ModelofTwistedBlades

Ablademodel withanairfoilsectionrepresentative ofawidechordfanwasusedtoproducetrainingsetsforthe

neural network.Thebladegeometry isofconstant crossectionandhasatwisthatvarieslinearlyfrom0degreesatthe

root,to30degreesatthetip.Bladedimensions arechord,8.72cm.(3.433in.);maximumthickmess tochordratio,0.037;

andspan,15.24cm.(6.0in.).Adamaged andanundamaged bladeweresimulatedwithfiniteelement platemodels.

Twofiniteelement blademodelsweregenerated, onewithasimulatedcrackandtheotherwithout.Thecrackis

locatedattherootandextendsfrom87%to100%ofchord.Thebladeswerestructurallymodeledascantileversby

constraining therootnodesinallsixdegreesoffreedom, except inthesimulatedcrackregion.Thecrackwassimulated

byreleasingtheconstraints foralldegreesoffreedomatthenodesinitsregion.Thefiniteelement modelshavea20x42

meshofquadrilateral elementsalongthemidthickmess of theairfoilsection(fig.4). The blade material is 6061 T6

Aluminum with a Young's Modulus of 66.19 GPa (9.6xl06psi), a Poisson's Ratio of .33, and a Mass Density of 2712.832

kg/m 3 (2.536x10 4 lbs sec2/in4). Figure 4 shows both the blade mesh and the simulated crack location.

MSC/NASTRAN Solution 103 was used to solve for eight normal modes and frequencies, although only the first

mode at about 199 Hz was used for this work. The eigenvectors were normalized with respect to the generalized mass. An

output file of the eigenvalues, eigenvectors (displacements), and modal strains was then provided to train the neural

network. A file of blade surface coordinates was also provided.

NASA/TM--1998-208814 4
4_:::'?::_'. • ""'_'5"x-_x"

-- 3.433" Crack Location

Fig. 4. Finite-element model of twisted blade showing crack location.

4. Performance of Neural Net for Detecting Blade Cracking for Measured and Model Generated Characteristic

Patterns

Tluee sample blades were manufactured according to the finite element design discussed in sec. 3 in order to rest

the performance of the neural networks. The blades were painted fiat white. The surface pattern fiom the brush strokes

proved m be non critical. Figure 5 shows that the blade and the blade mount are in fact macllined fiom the same block of

aluminum. The mount is held in a vice for subsequent tests. The vice torque is set to the same value for all tests. One of

the blades, henceforth called blade 1, was intentionally cracked by inducing high cycle fatigue failure Oll a shaker table

vibrating at the fiequency of the first mode (about 199 Hz). The crack developed in the region shown as an insert in

fig. 1. The other two samples, henceforth called blades 2 and 3, were undamaged.

Fig. 5. Twisted blade and blade mount.

NASA/TM--1998-208814 5
Initially, tests were performed oi1 file whole blade characteristic patterns of file first mode as shown in fig. 1.

Maximum displacement amplitudes ranged from a small fraction of a micron to 50 microns. An interferometer was used

to measure and set the vibration amplitudes. Hybrid training sets were used where the input characteristic patterns were

recorded experimentally as discussed in sec. 2. The output vector contained the chord wise second derivative of the

model generated field K'8 as discussed in sec. 2. Second derivatives were evaluated numerically. Neural networks

trained with the whole blade patterns generally could not distinguish the undamaged blades from the cracked blade. A

change in approach was required.

The test procedure was changed after noting that the resonant frequency of the cracked blade decreased from 199

Hz m 195 Hz as the vibration amplitude was increased. The frequency change first appeared when the amplitude of

vibration was increased above 50 microns. However, the contrast of the whole blade Bessel characteristic pattern became

too small when the vibration amplitude was increased above 50 microns. Hence, the decision was to zoom onto the insert

region shown in fig. 1 for subsequent measurements. The amplitude of vibration remained small and the pattern contrast

remained high in the zoomed region. The neural networks began to distinguish the cracked blade from the undamaged

blade at tip vibration amplitude of 50 microns. The settings for the remaining discussions were 273 microns. It should be

noted that the tip deflections were inferred at amplitudes larger than 50 microns. The amplitude was measured using the

interferometer near the insert region shown in fig. 1, and the finite element model was used to estimate the tip deflection

The measured region in the insert in fig. 1 extended span wise from the mount and chord wise to the right edge

of the blade. The width of the region equaled 0.5 in. (1.27 cm) and the height equaled 0.438 in. (1.111 cm). The

performances of two neural net architectures were tested. The composition of the training records was discussed in sec. 2.

The inputs consisted of experimentally measured patterns from blades 1 and 2. A 903 node input vector was used. Blade

1 was also used to generate zero amplitude training records. One hundred training records per blade or condition were

recorded in accordance with the ten percent rule mentioned in sec. 2. The two net architectures differed only in the output

vectors. The chord wise second derivatives of the model generated field K'8 were used in the output vector for one

architecture. A simple color coded output was used for the other architecture. In terms of normalized outputs, the code

was (0.8, 0.2, 0.2) for green, (0.2, 0.8, 0.2) for yellow and (0.2, 0.2, 0.8) for red. For display purposes at 30 frames per

second, the model generated output was displayed as a density plot. The color code, on the other hand, was used to set the

color of a 30 frame per second display of the characteristic pattern at CCD or finite element resolution. The color was

green for undamaged blades and red for cracked blades. The yellow color was used to indicate either the zero amplitude

(speckle noise only) condition or a no decision condition. The maximum component of the color vector was required to be

0.8+0.05, otherwise a no decision was declared. Both neural net architectures contained 6 hidden layer nodes.

(a) (b)

Fig. 6. Measured-region experimental characteristic patterns: (a) for undamaged blade,

(b) for cracked blade.

Figure 6 shows the measured region experimental characteristic patterns for the undamaged and cracked blades.

Figure 7 shows model generated characteristic patterns for an undamaged blade at two different finite element resolutions.

NASA/TM--1998-208814 6
41 X 85 51 X 101

Fig. 7. Model-generated characteristic patterns for an undamaged blade at two

finite element resolutions.

Boil1 neural net architectures performed well when presented wifll blade 1, 2 or 3 experimental characteristic

patterns at tip deflections of 273 microns. But neither neural network performed well when presented with the model

generated characteristic patterns.

5. Discussion of Performance of Neural Nets

The experimentally trained neural networks did not respond correctly to the model generated data, and the reason is

clear from comparing fig. 6 with fig. 7. The finite element model and experimental patterns are not similar in the

measured region.

In fact, the twisted blade results are disappointing in two ways when compared with previous work with cantilever

plates. First, twisted blade damage was not detectable from the entire blade characteristic pattern; whereas damage was

detectable from an entire cantilever plate pattern at low excitation amplitudes. The expectation, following the cantilever

plate work, was that the entire blade characteristic pattern would serve as a neural net processed gauge of blade damage.

Second, the finite element models used for design did not reproduce the correct twisted blade patterns. The models did

produce the correct cantilever patterns. Figure 8 (Decker et at., July 1997) compares model generated and measured

characteristic patterns for the first chord wise mode of a cantilever. The model generated and experimental patterns look

quite similar, and a neural network can be trained to distinguish damaged from undamaged blades using the entire

cantilever characteristic pattern.

Fig. 8. First chord-wise mode: (a) from a silver halide hologram of a vibrating blade, (b) from electronic

holograms of a vibrating cantilever, (c) from a finite-element-resolution model of a cantilever plate,

(d) from electronic holograms at finite-element resolution.

6. Concluding Remarks

Artificial neural networks could not be used to create a practical model to experiment interface when trained

with a design grade finite element model. The model could not create the detail that electronic holography can measure.

There are fracture mechanics models that generate better structural detail, but one of our objectives was to create a good

design to test interface. The workstation resident design grade models were not adequate for this pro-pose.

The nets trained with experimental inputs were able to detect structural damage and to display the damage using

model generated or damage flag outputs. Hence, the nets are good tools for testing whether a model is adequate: train the

nets experimentally and then use the model to generate inputs to be evaluated by the trained nets.

Experimentally trained nets can also be used to create a record of an undamaged part to be used for later

inspections.

NASA/TM--1998-208814 7
A questionrelevant tofllisconference concernsfilepotential usefulness (notmerelyfilepossibility) ofusing

modeltrainedneuralnetworksforflowapplications. Forexample, aremodeltrainedneuralnetworksusefulfor

performingtomography?Thebestjudgement attiffstimeis flintfl_eneural netapplicationcanbeusefulforflow

conditions flintremainessentially f_esameyearafteryear.Anexamplewouldbeawindtmmeloperatedessentially f_e

samewayforyears.Thenfl_eneural netcanbetrainedwifl_muchefforttorespondtofl_eimportant detailsandtoignore

fl_eirrelevancies, justasanetwastrainedtoignorefl_elaserspeckleeffect.

References

Decker, A. J., "Neural networks for calibration tomography", Optical Diagnostics in Fluid and Thermal Flo_t,

Proc. SPIE, vol. 2005, pp. 562 569, 1993.

Decker, A. J., Fite, E. B., Mehmed, O. and Thorp, S. A., "Processing speckle patterns wifll model trained

neural networks", Optical Technology in Fluid, Thermal, and Combustion Flo_t III, Proc. SPIE, vol. 3172,

pp. 285 293, July 1997.

Decker, A. J., Fite, E. B., Mehmed, O. and ThoFp, S. A., "Vibrational Analysis of Engine Components Using Neural Net

Processing and Electronic Holography", Advanced Non-Intrusive Instrumentation for Propulsion Engines,

AGARD-CP-598, pp. 33 1 33 6, May 1998.

Vest, C. M., Holographic Interferometry, pp. 177 217, Wiley, New York, 1979.

Goodman, J. W., "Statistical properties of laser speckle patterns", Laser Speckle and Related Phenomena,

Dainty, J. C., ed., pp. 29 35, Springer Verlag, Berlin, 1975.

NASA/TM--1998-208814 8
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November 1998 Technical Memorandum

4. TITLE AND SUBTITLE 5. FUNDING NUMBERS

Comparison of Computational-Model and Experimental-Example Trained

Neural Networks for Processing Speckled Fringe Patterns

6. AUTHOR(S)

A.J. Decker, E.B. Fire, S.A. Thorp, and O. Mehmed

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Lewis Research Center

Cleveland, Ohio 44135- 3191

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546- 0001

WU- 519-30-53 -00

8. PERFORMING ORGANIZATION

REPORT NUMBER

E-11413

10. SPONSORING/MONITORING

AGENCY REPORT NUMBER

NASA TM--1998-208814

11. SUPPLEMENTARY NOTES

Prepared for the International Conference on Optical Technology and Image Processing in Fluid, Thermal and Combustion

Flow, cosponsored by the Visualization Society of Japan and SPIE - The International Society of Optical Engineering,

Yokohama, Japan, December 7-9, 1998. A.J. Decker, E.B. Fire, S.A. Thorp, and O. Mehmed, NASA Lewis Research

Center. Responsible person, A.J. Decker, organization code 5520, (216) 433-3639.

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12b. DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of

damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally

generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of

the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been

trained in the past with computational-model-generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to

achieve, even with fan-blade fmite-element models.

14. SUBJECT TERMS

Neural networks; Interferometry, Electronic holography; Image processing; Speckle

metrology

17. SECURITY CLASSIFICATION

OF REPORT

Unclassified

NSN 7540-01-280-5500

15. NUMBER OF PAGES

14

16. PRICE CODE

A03

18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT

OF THIS PAGE OF ABSTRACT

Unclassified Unclassified

Standard Form 298 (Rev. 2-89)

Prescribed by ANSI Std. Z39-1B

298-102