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 33405 Talence cedex-France E-mail : batsale@lept-ensam.u-bordeaux.fr

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

• 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

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…

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

Metrology Imaging

Laboratory method

Sample

Thermocouple

1 T(t) measurement

0.1 T (°C)

10 20 30

t (s)

400

Industrial method

Sample

IRcamera

>10 000 pixels T(t) measurement

2.5 T (°C)

10 20 30

t (s)

400

Can we discern two very noisy thermograms ?

-0.5

0

0.5

1

1.5

0 20 40

Temperature level

Time (s)

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

Assumptions about the measurement noise

T = T + eT^

T = f (t, 12 , 3

i = i + ei^

explicative variable

Finite number of parameters

real value

measure (random variable)

” measurement error”

(ramdom variable)

estimator (random variable)

estimation error (random variable)

real value

Hypothesis :

-zero mean and additive errors

constant and unknown before the estimation and Xij 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

^̂ = (Xt X)-1 Xt T

cov(e) = (Xt X)-1 2

Estimator

Estimation error

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 storageBut:

-How to do the determination of the sensitivity matrix X ???-What kind of linear transform (Xt*T) can be chosen?

ESTIMATION AND LOCALLY 1D TRANSFER

Expression of the temperature response from a Flash experiment

2

12

/²²

exp21,0 LatfcL

Q

L

tan

cL

QtzT

n

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:

)/(0

20

20

/),0(cLt

ii

it

ft

cL

QcLtf

cL

QtT

)/(0

0

20

20

/),(cLt

ii

i

fcLtf

cL

QtLT

or

2

1

XT

In this case X or the sensitivity vectors are calculated theoretically with the knowledge of nominal parameters.

or

2

1

XT

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.

0 10 20 30 40 50

-20

-10

0

10

20

30

40

50

Temperature Level

Time (s)

One pixel thermogram

Average of the image thermogram

Experimental Thermogram with Raytheon Palm IR 250

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.

3D representation of the delamination (equivalent air thickness)

ee airair 2

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.

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.

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 excitationD. 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 1996.

• Flash method and asymptotic expansions estimations methodsMourand 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 1437-1441Goetz 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, 227-232, 2001.

• Space Fourier transformPhilippi 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), pp182-192.Krapez J.C., 1999 Mesure de diffusivité longitudinale de plaques minces par méthode de grille-Journée SFT:”Thermographie IR quantitative”

ONERA Mars 1999.

• 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, pp21-26Varenne 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).