1. S. MEZGHANI, E. PERRIN, V. VRABIE
An optimization method for evaluating variations in paint coating thickness by infrared thermography
S. MEZGHANI, E. PERRIN, V. VRABIE
CReSTIC-Châlons, EA 3804, IUT de Reims-Châlons-Charleville, Chaussée du Port, B.P. 541, Châlons- en-Champagne, France
J.L BODNAR
GRESPI/ECATHERM, Université de Reims Champagne-Ardenne, BP 1039, Reims, France
J. MARTHE, B. CAUWE
CRITT-MDTS, 3 Boulevard Jean Delautre - ZHT du Moulin Leblanc, Charleville-Mézières, France
Good morning lady and gentleman. I m sihem mezghani, thanks you to introduce me. This presentation outline our work on multiscale analysis of thermography imaging dynamic for sol-gel coating discrimination undertaken by collaboration between GRESPI and industrial research laboratory CRITT. The objective of our project is the characterization of surface coating and discriminate the sol-gel sub-layer presence among a mass of sample using thermography techniques.

2. Plan Context IR thermography technique
Experimental set-up
Data acquisition
Thermal enhancement approach: PLSR
Application
Thermal signature extraction
Calibration
Conclusions et perspectives
So, I will start this presentation by a brief presentation of the context and objective of this study, then I will expose the IR thermography experiment carried in this work and followed by a discussion of results of cross correlation analysis and wavelet pre-processing to sol-gel coating discrimination. We interest particularly to the effect wavelet function choice regarding discrimination performance. I finish this presentation by some conclusions and perspectives.

3. non-destructive methods
Context
Eddy Currents
Ultrasonic methods
Terahertz methods
non-destructive methods
Expensive
Slow image acquisition rate;
Requirement of refraction index;
Punctual;
Signal interpretation required;
Only effective on conductive materials;
Experience required to set up;
Accessibility of surface to sonic probe;
Difficulty to measure a thin paint coating
Drawbacks
Eddy current:
Not suitable for large areas and/or complex geometries. Large area scanning can be accomplished, but needs the aid of some type of area scanning device, usually supported by a computer, both of which are not inexpensive. The more complex the geometry becomes, the more difficult it is to differentiate defect signals from geometry effect signals.
Signal interpretation required. Due to the many factors which affect eddy currents, careful interpretation of signals is needed to distinguish between relevant and non-relevant indications
Only effective on conductive materials. The material must be able to support a flow of electrical current. This makes testing of fibre reinforced plastics unfeasible.
Will not detect defects parallel to surface. The flow of eddy currents is always parallel to the surface. If a planar defect does not cross or interfere with the current then the defect will not be detected.
Ultrasonic methods:
Surface of part to be inspected must be accessible to sonic probe
High degree of skill and experience required to set up and interpret results for varied test conditions
Terahertz method:
The main drawback is then a rather slow image acquisition rate which is not suitable for many applications.

4. Context Energy deposit
Camera IR
Heat source
Energy deposit
Paint coating
Substrate support
Evaluation of Heterogeneity of Paint Coating on Metal Substrate using:
Punctual Thermography: Laser
Unidimensional response;
Rapid interpretation of paint coating thickness;
Ref: (S.MEZGHANI,2015; S.MEZGHANI,2016)
Surface thermography: Flash
Bi-dimensional response;
High sensitivity to different experimental factor;
S. Mezghani, E. Perrin, J.L Bodnar, B. Cauwe, V. Vrabie: Evaluation of Heterogeneity of Paint Coating on Metal Substrate Using Laser Infrared Thermography and Eddy Current, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering Vol:9, No:5, 2015.
S. Mezghani, E. Perrin, J.L Bodnar, J. Marthe, B. Cauwe, V. Vrabie: Coating thickness evaluation based on Infrared thermography laser technique, Infrared Physics & Technology Journal, Elsevier,Vol.76, Pages 393–401, May 2016.

5. Pulsed thermography inspection
Context
Pulsed thermography inspection
Advantage
Limitation
Fast inspection rate;
Non contact nature;
Security and safety;
Imaging capability;
High sensitivity to external reflection;
High dependence between observations and thermal properties of inspected materials;
Non-uniform heat excitation ;
for coating quality inspection, two kinds of techniques can mainly be differentiated : non destructive and destructive testing.
NDT is the process of inspection and evaluating material component without the service ability of the sample.
In contrast to NDT, other tests are destructive in nature and are therefore done on a limited number of samples. This study is devoted to validate a new NDT using thermograph experiment for evaluating the paint thickness variation.
the advantage of Pt compared to other NDT technique is the cost associated to IR equipement, fast inspection which only fe’w second are required to perform the acquisition and analysis,
Removing/Reducing the undesired sources to best evaluate the paint thickness variation

6. Application: Thermal signature extraction
Experimental set-up
IR camera FLIR: FLIR SC655 model
good ratio price/performance
Heat pulse : Flash(2 kW)
Sample
Thermography method consist on heating wherein a painted samples by a flash lamp. In our study, each sample is excited during 1second. The sample temperature was recorded using IR camera during 10 seconds. A partial automated software system was employed to acquire temperature distribution of test sample.
Excitation processus:
Flash,
2 Flashes,
3 Flashes,
All Flashes.

7. Thermal data preprocessing
Substrate support
Paint coating
2D thermal data
Selection of inspected sample
Camera IR
Flash lamp
Substrate support
Paint coating
Selection of thermal response
Thermal excitation
Repeat Nx*Ny time
Multiplication process by square root of temporal series
Data acquisition of 3D Thermal data
Reconsruction of new thermal data
Unfolding of thermal data
Thermal data preprocessing
It is based on multiplying each row of the matrix with a time square root temporal function in accordance with the theoretical thermal response of bi-layered samples excited by a Dirac pulse excitation.
Application of PLSR

8. Thermal data preprocessing
3D thermal data
2D thermal data
2D Raster
like
Regrouping the thermal data
Heterogeneity of heating source in local sample
The setup consist of an IR camera, a manual flash lamp and a computer. All this temporal image sequence is stored into a single cube structure of data. The detection of thermal response is highly contaminated by several source of noise principally the non uniform heating which is considered as the major drawback of PT inspection technique.
Observation
Recorded image
Heating stage
Cooling stage
Variables
pixels

9. Thermal enhancement approach
Differents methods of multivariate regression for selecting factor component
Principal component regression (PCR):
explain as much predictor variation as possible
Reduced rank regression:
explain as much response variation as possible
Partial least square regression (PLSR)
the idea of used of partial least square is to find directons that have high variancean dhave high correlation whith the predictor y unlike general multiple regression pls regresssion can handle strong collinear data and the data and the data in which number of predictor is larger than the number of observation. It exist other linear feature extraction algorithm such aslinear discriminant analysis (LDA) canonical correlation analysis (CCA)
the pls build the relationship between response and predictors through a few latent variable contructed from predctors. Th number of latent variabls is much smaller than that of the original predictors
explain both as much response and predictor variation as possible

10. Thermal enhancement approach: PLSR
Flowchart PLSR algorithm process
Selection of parameters: predictor, predicted
Choice of number of factors (n)
Calculating of the first eigenvector by SVD decomposition
Repeat (n) time
Calculating of the factor by regression on predictor and predicted
All of the techniques implemented in PLS procedure work by extracting successive factors or linear combinations of predictors that optimally address one or both of these two goals : explaining response variation and explaining predictors variation. Partial least square balances two objectives seeking for factors that explain both response and predictors variation
Deflating these matrix by their residual

11. Application: Thermal signature extraction
Test sample
300 mm
2mm
70 mm
57 µm
69 µm
79 µm
Coated sample:
Coating layer: black epoxy resin (mat) with the thickness between 50 and 80µm,
Support : stainless steel with constant thickness around 2mm.
Thickness average
In this study, ten rectangular steel samples were considered. Only five samples were coated by thin film of sol gel, and then all samples were next painted with a commercial white paint. Look at cross section micrographs of this sample obtained by optical microscopy. The sol gel layer has 12 µm thick and paint layer has almost 50µm thick.

12. Application:Thermal signature extraction
« loading plot »
« Predictor »
« Number of PLS component »
Nx*Ny = Measurement
« Feature extraction »
Nt = Observation
« score plot»
« Predicted » 1 .
399
Nt= Observation
In this study, ten rectangular steel samples were considered. Only five samples were coated by thin film of sol gel, and then all samples were next painted with a commercial white paint. Look at cross section micrographs of this sample obtained by optical microscopy. The sol gel layer has 12 µm thick and paint layer has almost 50µm thick.
Assess the significance of loading component according of their corresponding score;

13. Application:Thermal signature extraction
Raw data
1 flash
2 flashes
4 flashes
T(K)
T(K)
T(K)
Filtered data by PLSR
1 flash
2 flashes
4 flashes
In this study, ten rectangular steel samples were considered. Only five samples were coated by thin film of sol gel, and then all samples were next painted with a commercial white paint. Look at cross section micrographs of this sample obtained by optical microscopy. The sol gel layer has 12 µm thick and paint layer has almost 50µm thick.
Overcoming of noise information related to experimental configuration of heat sources
Normalisation of thermal reponse to evaluate the thickness variation of paint coating

14. Application:Thermal signature extraction
Before PLSR
After PLSR
Offset
In this study, ten rectangular steel samples were considered. Only five samples were coated by thin film of sol gel, and then all samples were next painted with a commercial white paint. Look at cross section micrographs of this sample obtained by optical microscopy. The sol gel layer has 12 µm thick and paint layer has almost 50µm thick.
Energy dispersion according to the number of sources used in PT inspection
Same thermal response whatever the experimental configuration of PT inspection

15. Application: Calibration
How converting the normalized value to an estimated coating thickness??
Calibration process
linear relationship between the normalized value and estimated coating by Eddy current technique
In this study, ten rectangular steel samples were considered. Only five samples were coated by thin film of sol gel, and then all samples were next painted with a commercial white paint. Look at cross section micrographs of this sample obtained by optical microscopy. The sol gel layer has 12 µm thick and paint layer has almost 50µm thick.

16. Conclusions
The multivariate regression using PLSR improve the analysis of recorded thermal images.
Only one loading component represents the thermal response of the coating, whatever the experimental configuration.
All loading components from different experimental configuration provide the same thermal response while removing the noise information related to external source.
Future work will focus on the validation of the developed approach for non-conductive substrates materials as well (PVC, polypropylene…) and extend the study to larger coating thickness range.
To conclude,…….

17. Thanks for your attention Questions ?