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REAL TIME INFRA-RED IMAGE PROCESSING FOR THE DETECTION OF DELAMINATION IN COMPOSITE PLATES L.GUILLAUMAT*, J.C. BATSALE** and D. MOURAND*** *LAMEFIP-ENSAM.

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Presentation on theme: "REAL TIME INFRA-RED IMAGE PROCESSING FOR THE DETECTION OF DELAMINATION IN COMPOSITE PLATES L.GUILLAUMAT*, J.C. BATSALE** and D. MOURAND*** *LAMEFIP-ENSAM."— Presentation transcript:

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2 REAL TIME INFRA-RED IMAGE PROCESSING FOR THE DETECTION OF DELAMINATION IN COMPOSITE PLATES L.GUILLAUMAT*, J.C. BATSALE** and D. MOURAND*** *LAMEFIP-ENSAM **LEPT-ENSAM UMR CNRS 8508 ***Cellule „Themicar“ of LEPT-ENSAM Esplanade des Arts et Métiers Talence cedex-France

3 Thermal Non Destructive Evaluation principle Analysis of the transient images of temperature responses recorded with the camera and estimation of thermophysical parameters cartographies. Heterogeneous composite sample IR camera Rear face observation Heat excitation (halogen lamp) IR camera front face observation

4 Advantages -No contact -Sensitive to delamination (layer of poor thermal conductivity) But -such methods are time consuming and expensive -Infrared Cameras are noisy -the image processing is heavy It is here proposed to: -consider the new generation of infrared cameras -consider suitable image processing methods Main features of such thermal NDE

5 Some low cost devices Raytheon Palm IR 250, Indigo alpha, Boeing U3000 New Infrared cameras Some high performance devices CEDIP Jade III, FLIR, AMBER etc…

6 Thermal NDE- Illustration with the flash method 1 T(t) measurement measurement with contact very accurate measurement >10000 T(t) measurements measurements without contact very noisy measurements MetrologyImaging Laboratory method Sample Thermocouple 1 T(t) measurement 0.1 T (°C) t (s) 400 Industrialmethod Sample IRcamera > pixels T(t) measurement 2.5 T (°C) t (s) 400

7 Can we discern two very noisy thermograms ?

8 Linear transform Signal proportional to the temperature Great amount of data Low excitations Reduction of the measurement noise influence Reduction of the amount of data Knowledge of the transfer model Parameters estimation DATA PROCESSING STRATEGY

9 Assumptions about the measurement noise T = T + e T ^ T = f (t,  1  2,  3   i =  i + e  i ^ explicative variable Finite number of parameters real value measure (random variable) ” measurement error” (ramdom variable) estimator (random variable) estimation error (random variable) real value

10 Hypothesis : -zero mean and additive errors  constant and unknown before the estimation and X ij known without error -constant variance (  known) and uncorrelated errors Linear least squares (Maximum likelywhood theorem) ^ ^  optimum minimize the sum squares function S between theory and experiment ^^ T = X  ^^  = (X t X) -1 X t T cov(e  ) = (X t X) -1  2 Estimator Estimation error

11 T = * B X Linear processing of data About 20 Mbytes in 20s Sensitivity Estimation of a reduced number of parameters Advantages: -Reduction of the amount of data -Decreasing of the noise influence -Possibility of sequential processing without memory storage But: -How to do the determination of the sensitivity matrix X ??? -What kind of linear transform (X t *T) can be chosen?

12 ESTIMATION AND LOCALLY 1D TRANSFER Expression of the temperature response from a Flash experiment The delamination in a composite material act as a small thermal conductivity variation on the temperature response of each pixel. Two kinds of asymptotic expansions can be considered: or In this case X or the sensitivity vectors are calculated theoretically with the knowledge of nominal parameters. or In this case X or the sensitivity vectors are calculated with a reference signal f(t). Such reference signal can be obtained here with the spatial average of the images of temperature.

13 Temperature Level Time (s) One pixel thermogram Average of the image thermogram Experimental Thermogram with Raytheon Palm IR 250

14 Application of this method with a CEDIP camera to the study of a delaminated sample Video image of a 5mm thick transparent delaminated fiber-glass–epoxy plate Delamination cartography estimation by thermal method of the previous plate.

15 3D representation of the delamination (equivalent air thickness) Relation between thermophysical properties and structural properties: It can be noted that in the centre of the damaged zone induced by impact exists an undamaged area observed also by de-ply technique.

16 Conclusion The main features of such NDE method are: The experiment is simple and contactless. The processing consists in computing weighted sums of the images or of the pixels. This can be done in real time (20s). The method can be implemented with low cost or high performance cameras The main points for the study of damaged samples : Such a device provides a 3D representation of the delamination in good agreement with physical destructive observations. Some future works will consist in observing the evolution of the delamination structure during fatigue experiments.

17 Bibliography about similar image processing methods (based on linear transform of the data and some physical knowledge about the heat transfer) Time Fourier Transform-Periodic excitation D. Wu, C. Y. Wu, G. Busse, Investigation of resolution in lock-in thermography: Theory and experiment, Eurotherm Quantitaive Infrared Thermography QIRT’96,Stuttgart 2-5 Septembre Flash method and asymptotic expansions estimations methods Mourand D., Batsale J.C.:(2000) Real time processing with low cost uncooled plane array IR camera-Application to flash non-destructive evaluation, QIRT 2000, Eurotherm seminar 64, Reims. Mourand D., Batsale J.C., Gounot J. :(1998) New sequential method to process noisy yemperature response from flash experiment measured by infrared camera, Review of Scientific Instrument, vol 69 n3, pp Goetz C., Batsale JC, Mourand D – Fast processing methods for thermal non-destructive evaluation of thin plates with low cost infared cameras. Image Analysis & Stereologie 20, (2) Suppl 1, , Space Fourier transform Philippi I., Batsale J.C., Maillet D. et Degiovanni A. : (1995) Measurement of thermal diffusivity through processing of infrared images processing, Rev. Sci. Instru., 66(1), pp Krapez J.C., 1999 Mesure de diffusivité longitudinale de plaques minces par méthode de grille-Journée SFT:”Thermographie IR quantitative” ONERA Mars Homogenization: Batsale J.C.., Gobbé C., and Quintard M., 1996, Local non-equilibrium heat transfer in porous media. Recent Res. Devel. in Heat, Mass & Momentum Transfer 1. Poncet E., Bereziat D., Grangeot G., Batsale J.C. (1998) Experimental estimation of the heat exchange coefficient of a non-equilibrium model by infrared measurement temperature on a stratified system- 11 Int.Heat Transfer Conference Kyong Ju Korea. Varenne M., Batsale J.C., Gobbé C., (2000) Estimation of local thermophysical properties of a 1D periodic heterogeneous medium by infrared image processing and volume averaging method- Journal of Heat Transfer-ASME. February 2000, vol 122, pp21-26 Varenne M., Batsale J.C., Gobbé C., (2000) Estimation of a local 1D or 2D thermal conductivity field with infrared images processing and volume averaging method, QIRT 2000, Eurotherm seminar 64, Reims. 2D thermal intercorrelation study: Guillaumat L. Davy L. Bouquet J. Batsale JC., (2003) A new thermal method for the crack detection in damaged composite plates-application of flash method and infrared thermography- Comp test 2003 communication (poster).


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