I. Sinusoidal excitation: experimental Thermographic Inspection of Composites Shining light on a hot topic Gaétan POELMAN, Saeid HEDAYATRASA, Joost SEGERS, Javier CALDERON, Wim VAN PAEPEGEM and Mathias KERSEMANS Mechanics of Materials and Structures (MMS), Ghent University, Technologiepark 903, B-9052 Zwijnaarde, Belgium. E-mail: [email protected] II. Pulse excitation: numerical Thermography is a novel approach in the field of non-destructive testing (NDT). A thermal excitation is applied to a carbon fiber reinforced plastic (CFRP), and the surface temperature development is recorded using a high-sensitivity thermal camera. Internal defects such as delaminations and voids cause a disruption in the heat diffusion, and can thus be detected in the form of a heat gradient. I. Sinusoidal excitation: experimental • Thermal excitation wave = sine wave Ø Specific frequency and amplitude Ø Sine conservation: amplitude and phase shift in readings ü Contrast • Governing equation: ℎ = 1.8 * +, Ø Lower excitation frequency f ➞ deeper probing depth • Detection limit due to 3D thermal diffusion and dampening IV. Prospects • Implementation of fusion of post-processing techniques • Start experiments on pulsed thermography • Make a critical comparison between various post-processing methods Reflection mode Ø Camera and lamp: same side Ø For shallow defects Sample: 8 layers UD CFRP Internal defects: Teflon inserts A. Raw thermal images 6 layers CFRP, inserts between layers 4 and 5 (non-uniform heating) C. Principal Component Analysis (PCA) Project raw thermal data onto empirical orthogonal functions (EOF) ➞ large data compression B. Fast Fourier Transform (FFT) Decompose raw thermal data with Fourier analysis D. Thermographic Signal Reconstruction (TSR) Fit polynomial to raw thermal data and compare polynomial coefficient images Noise reduction and data compression by applying different post-processing methods III. Conclusions • Thermography is powerful tool for defect detection • Thermal wave frequency inversely proportional to depth probing • Many useful post-processing techniques Full thermographic set-up 1.5 Hz 0.2 Hz 0.4 Hz 0.75 Hz Modelled CFRP with 7 defects The polynomial coefficients contain information FFT analysis cancels out surface properties’ fluctuations First three EOF’s largely represent original data set Defects become visible after some time Acknowledgement The work leading to this poster has been funded by the SBO project “M3DETECT-IV”, which fits in the MacroModelMat (M3) research program funded by SIM (Strategic Initiative Materials in Flanders).

Thermographic Inspection of Composites · III. Conclusions • Thermography is powerful tool for defect detection • Thermal wave frequency inversely proportional to depth probing

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Page 1: Thermographic Inspection of Composites · III. Conclusions • Thermography is powerful tool for defect detection • Thermal wave frequency inversely proportional to depth probing

I.Sinusoidalexcitation:experimental

ThermographicInspectionofCompositesShininglightonahottopic

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

E-mail:[email protected]

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 *+,

�

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).