Thermographic Inspection of Composites · III. Conclusions • Thermography is powerful tool for...

I.Sinusoidalexcitation:experimental

ThermographicInspectionofCompositesShininglightonahottopic

GaétanPOELMAN,SaeidHEDAYATRASA,JoostSEGERS,JavierCALDERON,WimVANPAEPEGEMand MathiasKERSEMANSMechanics ofMaterials and Structures (MMS),Ghent University,Technologiepark903,B-9052Zwijnaarde,Belgium.

E-mail:gaetan.poelman@ugent.be

II.Pulseexcitation:numerical

Thermographyisanovelapproachinthefieldofnon-destructivetesting(NDT).Athermalexcitationisappliedtoacarbonfiberreinforcedplastic(CFRP),andthesurfacetemperaturedevelopmentisrecordedusingahigh-sensitivitythermalcamera.Internaldefectssuchas

delaminations and voids causeadisruptioninthe heatdiffusion,andcanthusbedetectedintheformofaheatgradient.

I.Sinusoidalexcitation:experimental• Thermalexcitationwave=sinewave

Ø SpecificfrequencyandamplitudeØ Sineconservation:amplitudeandphaseshiftinreadings

ü Contrast

• Governingequation: 𝑑𝑒𝑝𝑡ℎ = 1.8 *+,

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Ø Lowerexcitationfrequencyf➞ deeperprobingdepth• Detectionlimitdueto 3Dthermal diffusionanddampening

IV.Prospects• Implementationoffusionofpost-processingtechniques• Startexperimentsonpulsedthermography• Makeacriticalcomparisonbetweenvariouspost-processingmethods

ReflectionmodeØ Cameraandlamp:samesideØ Forshallowdefects

Sample:8 layersUDCFRPInternaldefects:Tefloninserts

A.Rawthermalimages6 layersCFRP,insertsbetweenlayers4and5(non-uniformheating)

C.PrincipalComponentAnalysis(PCA)Projectrawthermaldataontoempiricalorthogonalfunctions(EOF)➞ largedatacompression

B.FastFourierTransform(FFT)DecomposerawthermaldatawithFourieranalysis

D.ThermographicSignalReconstruction(TSR)Fitpolynomialtorawthermaldataandcomparepolynomialcoefficientimages

Noisereductionanddatacompressionbyapplyingdifferentpost-processingmethods

III.Conclusions• Thermographyispowerfultoolfordefectdetection• Thermalwavefrequencyinverselyproportionaltodepthprobing• Manyuseful post-processingtechniques

Fullthermographicset-up

1.5Hz

0.2Hz0.4Hz

0.75Hz

ModelledCFRPwith7defects

Thepolynomialcoefficientscontaininformation

FFTanalysiscancelsoutsurfaceproperties’fluctuations

FirstthreeEOF’slargelyrepresentoriginaldataset

Defectsbecomevisibleaftersome

time

AcknowledgementThe work leading to this poster has been funded by the SBO project “M3DETECT-IV”, which fits inthe MacroModelMat (M3) research program funded by SIM (Strategic Initiative Materials inFlanders).