LASER CLADDING PROCESS AND IMAGE PROCESSINGle2i.cnrs.fr/IMG/publications/130_Lasers in engineering-97.pdf · LASER CLADDING PROCESS AND IMAGE PROCESSING F. MERIAUDEAU* F. TRUCHETET*

Journal of Lasers in Engineering, vol.6, pp. 161-187, 1997

1

LASER CLADDING PROCESS AND IMAGE PROCESSING

F. MERIAUDEAU* F. TRUCHETET* D. GREVEY** and A.B. VANNES ***

* Laboratoire GERE - Université de Bourgogne- 12 rue de la Fonderie- 71200 Le Creusot - France.

Tel : (33) 85-80-30-30, Fax : (33) 85-80-36-15, email: [email protected]

** Laboratoire ThemoMécanique - Université de Bourgogne- 12 rue de la Fonderie- 71200 Le Creusot - France.

Tel : (33) 85-80-30-30, Fax : (33) 85-80-36-15

*** CALFETMAT, MMP, Ecole Centrale de Lyon, BP 131, 69131 Ecully, France

ABSTRACT

The laser cladding process involves many processing parameters. We present in this paper a low cost system based on two

CCD cameras, a standard acquisition card and a Personal Computer which enables the operator to interact with the process

and processing parameters.

Geometrical information can be extracted such as the height or the width of the track.

Moreover, one of the camera is used to carry out temperature measurements. In addition we present three different methods

of measuring the speed of the particles in the powder spray.

INTRODUCTION

The laser cladding process, which consists in adding a melted powder to a substrate in order to improve or change the

behaviour of the material against one or several surface agressions, involves a lot of parameters. In order to perform good

deposition some parameters need to be controlled during the process.

In the first part we will see how the operating parameters influence the shape of the tracks. We will give a short summary of

what one can find in the literature and we will also present the results obtained with our experimental set-up.

CCD cameras have already been used to obtain information during processes such as laser welding, laser cladding or

drilling. We shall show that a very cheap system based on a standard frame grabber,two CCD cameras and a Personal

Computer can be used to provide the operator with important information during the laser cladding process.

In the second part of the paper, we show that one camera previously calibrated with a black body enables the surface

temperature to be determined. Moreover, the surface temperature can be used, using Beer Lambert’s law, to detect

variations in the powder mass feed rate. With such a system it is possible to detect a fluctuation of 2 to 3g/min in the mass

flow rate. The other camera provides information related to the powder distribution. A simple algorithm applied to the data

acquired from the CCD matrix camera enables very weak fluctuations within both gas flows (carrier or shroud gas) to be

observed.


2

As described in part four, this camera is also used to carry out geometrical measurements during the process. Geometrical

parameters such as the height (build up), the width as well as the Half Height Width of the track are obtained in real time

and enable the operator to obtain information related to the process parameters such as the speed of processing or the mass

flow rate.

As is known the speed of the particles is also a very important parameter. Indeed, this determines the irradiation time of the

particles before reaching the surface, thus leading to different states of the powder, such as liquid or solid. If the particles

are still solid, the irradiation time determines whether or not the particles have a uniform. In this part a special emphasis is

placed on speed measurement. Indeed three different methods involving CCD cameras are presented measuring the speed

of the particles. The accuracy of the different methods and the computation time required by those methods is discussed.

In the last section the work is summarized and future developments discussed.

IMPORTANT PARAMETERS

In many industrial applications, the surface is the determining factor in the life time of the component. In such cases,

it is important to be able to improve the surface properties [1] to [4]. One of the many known solutions [5] to [8], is to

deposit a specific alloy which has the requisite properties. This is termed cladding. The main problem with this technique is

the number of parameters and their influence on thequality of the clad tracks.

Many investigations have been devoted to establish which parameters need to be controlled during the process in order to

improve the quality of the track.

We cite here some articles, [9] to [12], from an exhaustive literature. It can be stated that the main parameters are the speed

processing, the powder feed rate , the laser power and the beam diameter. Depending on the configuration adopted:

predeposed powder, coaxially blown powder, laterally blown, the parameters do not have the same effect. Moreover, if for

example a coaxially blown powder system is used, one must control the carrier gas flow and the shroud gas flow. Small

variations in the powder flow rate result in large fluctuations in the geometry and the microstructure of the tracks produced.

At slower processing speeds (see figure 3), the surface reaches a higher temperature, leading to a deeper penetration

and thus a higher dilution and lower specific properties.

Judicious combination of the treatment parameters such as the laser power, the beam diameter, the processing speed

and the powder feed rate enables the operator to control the process in order to obtain a dense cladding with a good

metallurgical bond to the substrate.

Variations in the parameters both lead to variations in the general shape of the tracks [13], [14], and also in the

microstructure [15], [16].


3

0

0,5

1

1,5

2

2,5

3

0 200 400 600 800 1000 1200 1400

Output Power (w)

su

rface (

mm

2)

figure 1: Variation of track section plotted against output power of the Nd:YAG laser

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

0 5 10 15 20 25 30 35

mass flow rate (g/min)

su

rfa

ce

(m

m2

)

figure 2: Variation of track section plotted against powder flow rate

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5

processing speed (mm/s)

se

cti

on

(m

m2

)

figure 3: Variation of track section plotted against processing speed


4

In order to reduce fluctuations and improve the reliabilty of the product, many researchers have trying to find good and

reliable sensors. During the last few years many sensors have been tried but, the high cost of such equipment, the lack of

real time or on line control has led manufacturies to seek other solutions.

Some information in the literature shows how to control the laser power using an integrating sphere [9]. The powder feed

rate can be controlled during the process using various methods: Grunenwalds and al [17] proposed a powder delivery

system characterized by high flexibility and accuracy, Steen [18], [19], [20] and others [21], [22] used optical methods.

High speed CCD camera are sometimes used and spectroscopy are required in the case of a plasma creation.

Thus ways have been developed in order to control the mass feed rate, surface temperature, laser power and other

pertinent parameters, involved in the laser cladding process over the past few years. However the price of this kind of

sensors is still too high.

We shall see below that the use of two CCD matrix cameras and some image processing tools is sufficient to detect

variations of the main parameters.

TEMPERATURE MEASUREMENTS

In 1901 Max Planck developed an equation which describes a relationship between the intensity of electromagnetic

radiation and its wavelength for any surface temperature. Planck's equation forms the basis for works done the field of

radiometry.

Max Planck's equation describes a continuum of radiation wavelengths (Fig. 4). Due to the temperature range in

laser cladding our region of interest lies between 0.7 and 15 µm, the part of the electromagnetic spectrum in which most

radiometric surface temperature measurements are made.

0.1

1

10

100

1000

10000

100000

1000000

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Wavelength (µµµµm)

Spectric energetic radiance (W

/m2/sr/ µµ µµm)

T=3000K

T=1800K

T=1200K

T=800K

T=500K

T=300K

Figure 4: Graphical representation of Planck’s law


5

As we have shown earlier in references [24], [25], the surface temperature is a very important parameter which needs to be

controlled. In order to provide contactless temperature measurement various radiometers can be used [26], but the use of a

CCD camera enables the operator to get an average temperature as does a radiometer but it also provides temperature

information for some precise areas.

The calibration of the temperature measurement system was described in our references [28], [27]. The principle is

to use the luminance emitted by the component and applying Planck’s law to be able to calculate the temperature[29], [30].

Since we do not require absolute measurements, errors made on the emissivity do not detract the efficiency of our system

[31]. During cladding the camera scans the melt pool and provides an indication of the temperature. The surface

temperature shows if a temperature high enough in order to melt all the particles has been reached. This is very important

when different powders with different fusion temperatures are mixed since unmelted particles can initiate cracks in the clad

leading to poor mechanical properties.

Real time surface temperature measurements are performed using a simple algorithm based on the stastitical

momentum conservation [32] (Wen’s algorithm) which detects the two gaussians (cold and hot area) provided by the

histogram(see figure 5).

[ ]MN

f x yl

l

ji

= ∑∑1

( , ) (1)

Ml is the momentum of order l, N the number of pixels, and f(x,y) the grey level intensity of the pixel based on x=i

and y=j.

If Pi is the proportion of pixels with the grey level intensity i (Pi=ni/N), one can write the four first momentums of

the image:

M0=1 (2)

M P ii

i

i

1

0

255

==

=

∑ * (3)

M P ii

i

i

2

2

0

255

==

=

∑ * (4)

M P ii

i

i

3

0

2553=

=

=

∑ * (5)

A binarized image has only two populations Z1 and Z2. If one notes q1 (respectively q2) the pixel proportion of grey

level Z1 (respectively Z2); if one looks at the statistical momentum conservation; the system which needs to be solved is:

M q z q z

M q z q z

M q z q z

M q z q z

0 1 1

0

2 2

0

1 1 1

1

2 2

1

2 1 1

2

2 2

2

3 1 1

3

2 2

3

= +

= +

= +

= +

(6)

After having found the linear system from the above non linear equations (6). The new system which has to be

solved can be written:


6

M C M C M

M C M C M

0 0 1 1 2

1 0 2 1 3

+ = −

+ = − (7)

C0 and C1 being known, one can evaluate the solutions Z1 and Z2 from the following equation

Z C Z C2

1 0 0+ + = (8)

When Z1 and Z2 are found, it is very simple to calculate q1 and q2 from the two first equations of the system 6. Thus

knowing q1 and q2, Wen’s threshold is obtained when the repartition function is equal to q1. As soon as wen’s threshold has

been figured out, the average grey level (corresponding to the temperature due to the linear relation of the temperature and

the grey level in our system [26], [27]) is provided by the usual relation:

G

G Nb

Nb

i i

i Wen threshold

i

i Wen threshold

= =

=

∑

∑

*'

'

255

255 (9)

where:

Gi is the grey level i

Nbi is the number of pixels having level value i

0

500

1000

1500

2000

2500

3000

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250

Grey level intensity

Nu

mb

er

of

Pix

els

Histogram of the image

Wien's Threshold

Figure 5. Temperature histogram

As said before, the melt pool temperature is very important, because it tells to the operator if the melting

temperature of all powder components has been reached. Moreover due to the fact that our CCD camera is placed at 45°

(see figure 9) above the scene we are also able to detect variations in the processing parameters. Indeed, an increase in the

mass feed rate lead to a decrease in the transmitted energy. In addition to this the track has a higher build up, thus a weaker

area orientated to the camera.


7

As one can see on the graphics displayed below our system enables us to detect very weak variations in the mass flow rate

(2 to 3 g/min (see figure 6).

The authors would like to say here, that the results presented on the temperatures require an average on 3 images (120ms)

This is due to the movements within the melt pool and the presence of bursting bubbles (see figures 7 and 8) during the

process which lead to wide temperature variations. Thus in order to make some good comparisons, it is required to average

over 3 images to smooth the temperature variations due to the different movements.

100

110

120

130

140

150

160

170

180

190

200

7 9 11 13 15 17 19 21

mass feed rate (g/min)

gre

y level in

ten

sit

y

Figure 6: Variation of the grey level intensity (temperature)

versus the mass feed rate.

Figures 7 and 8: visualisation of the melt pool.The lap between the two images is 40 ms.

One can easily see the temperature difference due to pool movements and bursting bubbles.

Bursting bubbles


8

figure 9: Set up of the experiment

POWDER SUPPLY

We use another CCD camera in order to check other different parameters. Using a coaxial delivery system [8], [10] we have

to cope with variations occuring in the carrier gas flow or with the shroud gas flow. Once one has set an adequat carrier gas

flow, fluctuations in the shroud gas flow induce large variations in the powder spray geometry and modify strongly the

process results.

In order to help the operator to set up correctly the shroud gas flow, we developed a short acquisition system. For

this, we take some acquisition of the powder spray geometry using a CCD camera which is placed in front of the cladding

nozzle. Experimentally, the substrate is located at 18 mm underneath the nozzle to insure a good quality coating [8]. On the

acquired images, we realize an average in order to remove some points which are not reprensative of the distribution (this is

done in order to perform this task automaticaly).The averaged image of the cross section is thresheld so that the width of

the powder spray can be measured automatically. Some of the results obtained are displayed on the figure 12, as one can

see the technics enable us to get very accurate information relative in this case to the shroud gas flow.

temperature measurements

Shape

measurements

(grazing angle)

45 °


9

0

50

100

150

200

250

0 50 100 150 200 250 300 350

length (pixels)

gre

y L

evel In

ten

sit

y

Figure 10: Transversal cross section of the powder beam of a single image

0

20

40

60

80

100

120

140

160

180

0 50 100 150 200 250 300

length (pixels)

gre

y l

ev

el

inte

ns

ity

Figure 11: Transversal cross section of an average of three images

0

50

100

150

200

250

300

350

400

450

500

2 3 4 5 6 7 8 9 10

gaz flow rate (l/min)

wid

th o

f th

e p

ow

der

beam

(pix

els

)

Figure 12: Variations of the width powder beam versus the shroud gas flow

the correct value corresponds to image 2

Not representative and very hard to do not

to take into account in an automatic

processing

threshold

width of the powder spray

1

2

3


10

SHAPE MEASUREMENTS

CCD cameras have found many applications in the field of measuring the geometrical characteristics of an object,

and reveal sometimes paramount information which are not accessible by another meaning. Indeed, as we have shown

through the aforedisplayed curves 1 to 3, we have a changement in the track section when variations in the parameters

occur. Moreover, in order to be more accurate we display below on the figure 13 the variations of the height of the tracks

versus the variations of the important parameters such as the processing speed.

In order to control these parameters during the process, the CCD camera is placed at a grazing angle. Thus the

camera can see the formation of the tracks in real time. Using these acquisitions we are able to detect variations of the

height and of the width of the track.

Those parameters are provided by the use of an edge detector [34] (Roberts) and an algorithm which is able to find

the width of the tracks (the wider part) and knowing the width one can find the height. The algorithm can also easily

measures the Half Height Width (figure 14-15) as well as the cross section of the track and consequently we determine the

process efficiency.

As shown on the results (see figure 11) the system is able to detect very small in the processing speed.

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0,16 0,18 0,2

processing speed (m/min)

He

igh

t (m

m)

Série1

Série2

Série3

Série4

Results found by

postmortem

metallurgical

analysis

Found by image

processing

Figure 13. Variation track height provided

by the CCD camera versus the processing speed


11

figures 14 and 15: image acquired at a grazing angle. Results after application of an edge detector

Due to the fact that an ambiguity may exist on which parameter varies, we add another sensor to check the mass feed

rate. The sensor is an optical sensor (photovoltaïc cell) to which a laser beam going through the powder stream is directed.

Using an electronic amplification and a serial liaison (RS 232) between the sensor and a Personal Computer we are able to

record the mass feed rate variations.

SPEED OF THE PARTICLES

The speed of the particles is a very important parameter involved in the process. It defines the interaction time of the

particles with the laser beam before reaching the substrate surface. If one looks at the heat equation which can be applied

herein to an homogeneous, isotropic and limited medium; assuming that no heat sources and no well of heat within the

particle exist, one can write the equation as follow:

( )∆TT

t k

dk

dT gradT− + → =1 1

0α

∂

∂ (10)

where:

∆T is the temperature Laplacian

α is the thermal diffusivity (m2.s

-1)

k is the thermal conductivity (W.m-1.K

-1)

Assuming that the thermal conductivity does not vary with temperature, Huetz [35] has solved the heat equation for

a sphere and has given the solution for a fast increase of the temperature at a point M’ situated at a distance r from the point

source M (r = diameter of the particle). The temperature can be written:

Tk

t

er t

r

t

( , )

( )

=

−

α

α

3

2

4

2

(11)

After differenciation, the time when the temperature is maximal at the point M’ will be:

width height


12

tr

M =2

6.α (12)

Thus, knowing the speed of the particle we are able to know if the particles have an homogeneous temperature when

they reach the surface. This parameter is important in order to prevent the apparition of cracks due to the non-melted

particles.

As one can see on the table I, the required time for the particles to reach an homogeneous temperature while

reaching the surface is around the tenth of the milliseconde. As we shall see above, the irradiation time in our experimental

set-up is greater than this value, leading to a good quality track.

Stellite 6

r = 60µm 1.58.10-4 s

r = 80 µm 2.81.10-4 s

Table 1: Required time in order to get an homogeneous

temperature within the particle

Sometimes with an output laser power important enough, the particles can reach the fusion temperature before

reaching the surface. The fact is very important, indeed in the paper [36] we can note that when the powder has reached the

fusion temperature, the relative required energy in order to assure a good quality coating (strong metallurgical bond) is

weaker than for non melted particles during the flight.

Thus if one looks at the energy transfer, the internal energy variation within a powder grain can be written as:

∆ ∆ ∆ΗQ m C Ti p f= +( . . )δ (13)

with:

m: mass of the particle (kg)

Cp: Specific heat (kg.m-3)

∆Η f : Fusion enthalpy (J.kg-1)

δ : step function = 1 if T>Tf

= 0 if T<Tf

According to our assumption, we can write that the internal energy variation is equal to the absorbed energy. As a

matter of fact, it can be written as a fonction of the incident energy :

dQ I A S dtabs o p p= (14)

where :

Io the irradiance (W.cm-2)

Ap absorption coefficient


13

Sp = πd2/4 , d diameter of the particle

dt: interaction time (s)

Ap is a very complex coefficient changing both with the temperature and the wavelength as well as with the surface

state of the target. We assume a value of 0.47 for Ap from previous experiment [8].

We would like to say here that we assume that the system is adiabatic and we do not take into account the energy

losses due to heat transmission (Boltzman) and heat convection (Newton).

On the graph displayed below (fig 17), we plotted the particle temperature versus the irradiance for different

irradiation time. As one can see, the speed of the particles which is directly linked to the irradiation time is a very important

parameter, especially for particles with a high absorption coefficient.

0

500

1000

1500

2000

2500

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Irradiance (W/cm2)

Tem

péra

ture

(°C

)

Flight Time 7,2 10-3 secondesv=2,5 m/s

Flight Time 6,5 10-3 secondes

v=2,76 m/s

Figure 17: Evolution of the powder temperature

versus the laser power irradiance for various interaction time

In order to investigate the speed of the particles we just used one CCD camera, which is cheaper than a set up

involving a Doppler anemometer. We would like to say here, that some work have used this kind of system [9], [33], but all

of them have worked in a very low mass feed rate range in order to detect speed or/ and trajectory at the center of the

powder spray. Using these feed rates they were not able to obtain realistic tracks. As the previous quoted authors we have

tried, using a strobe and an algorithm which was able to detect trajectories, to get information related to the speed of the

particles. As one can see on the figure 18, it is easy to determine trajectory and speed of excentric particles which does not

participate in the cladding process, but it is impossible to investigate the center of the powder spray which is the really

interesting part of the stream..


14

figure 18: trajectory and particle speed

In order to investigate the speed of the particles both within the core of the powder distribution and with a

reasonable mass feed rate, the authors used a CCD camera with a variable integration time (electronic shutter) and a laser

light-sheet in order to inspect the powder distribution (Schlieren method)[37]. After having calibrated the camera, it is very

easy for the operator to find the speed of the particle knowing the selected integration time. Indeed the measure consits in

the determination of the trace length in a given direction. Knowing the lenght, the operator finds thanks to the calibration

the real length, and he can determine the speed because in knows the integration time. The results are displayed below

(figure 19). Comparison with the results found using a doppler anenometer shows a great accuracy of the system.

0

0,5

1

1,5

2

2,5

3

0 2 4 6 8 10 12 14 16 18 20

distance nozzle/sensor (mm)

sp

eed

of

the p

art

icle

s (

m/s

)

Image processing Doppler

Anemometer

Acquired Image

0

50

100

150

200

250

0 5 10 15 20 25 30 35 40

Série1

Figure 19. Variation of the particle speed versus the distance nozzle/sensor

and comparison with the results provided by a Doppler Anemometer

The main problem of this method is the fact that the operator is required to achieve a cross section (as displayed on

the above graphic). In view to avoid this disadvantage, we developed an automatic extraction procedure. This method is

similar to the one employed by Galloway [38] for texture analysis. After having proceeded to a binarisation of the image we

count the number of traces, their lengths and their starting point. This is done using a block color (8 neighboors) algorithm

[39]. As a result, we obtain a matrix which contains for each starting point and each length, the number of pixels. Those

data enables us to withdraw nonprobable data (particles crossing the lighting plane).


15

In order to alleviate the experimental set-up we have tried another method: calculate the Discrete Fourier Transform of an

image strobed with two flashes. Indeed, if one assumes the following theoretical model for the powder modelization, one

finds that it is possible to have access to the speed of the particles while knowing the monodimensionnal DFT.

Indeed if one assumes that a particle can be represented by a known profile, gaussian for instance (15), and that their

repartition along a line follows a Poisson’s distribution δ al x( ) which probabilty follows the expression (16).

f x e x( ) = −π 2

(15)

P NN

eN

( , )( )

!τ

λτ λτ= − (16)

where λ is the average evenement number by space unity and P N( , )τ represents the probabilty of finding N particles in

the space τ .

One line of the image can be represented by the expression:

I x f x xal1 ( ) ( ) * ( )= δ (16)

where * is the convolution product symbol.

The signal due to the particles which as moved (strobed) (I2 (x)) can be written:

I x f x xd al2 ( ) ( ) * ( )= δ (17)

where d means the translation done by the particles.

Thus the signal captures after the second strobe flash can be expressed by:

I x I x I x( ) ( ) ( )= +1 2 (18)

If one expresses the previous equation in the Fourier space, this would lead to

I I I∧ ∧ ∧

= +( ) ( ) ( )υ υ υ1 2 (19)

where I∧

( )υ is the Fourier transform of I(x)

Expression (19) can also be written as follow:

I f ealj d

∧ ∧ ∧−= +( ) ( ) . ( ) ( )υ υ δ υ π υ1 2

(20)

Leading to:

I f eal

j d( ) ( ) . ( ) .υ υ δ υ π υ∧ ∧ ∧

−= +2 2 2

22

1 (21)

Or:

I f dal( ) ( ) . ( ) .( cos( )υ υ δ υ πυ∧ ∧ ∧

= + −2 2 2

2 1 2 (22)


16

If one investigates, the special frequencies for which the expression (22) is equal to zero, one has to solve:

f eal

j d( ) . ( ) .υ δ υ π υ∧ ∧

−+ =2 2

22

1 0 (23)

The three terms of the expression (26) can be studied separately. On the references [40], [41], [42] one can find that :

f e( )υ πυ∧

−=2

22

(24)

and

δ υ λ δ υ λal ( ) ( )∧

= +2

2 (25)

Thus the resolution of the equation leads to:

f e dj d( ) .( ( ) ). cos( )υ λ δ υ λ πυπ υ∧

−+ + = ⇔ + =2

2 22

1 0 1 2 0 (26)

which gives:

f e dk

kj d( ) .( ( ) ).υ λ δ υ λυ υ

π υ∧

−+ + = ⇔ = + ∈2

2 22

1 01

2Ζ (27)

thus the monodimensional square module of the DFT enables to get information to the translated distance. Knowing the

time between to flashes, we are able to find the average speed of the particles.

The main problem linked to this method, is the fact that the translation has to be very small to prevent both the effect of the

acceleration and to find a too small frequency which could be confounded with the noise of the image. The authors have

been able to simulate this (see figures 20 to 24), but were not experimentally able to strobe the signal fast enough in order

to obtain expected results. But,we think that this method could be very accurate and fast (using DSP circuits for FFT

calculation) with a more convenient experimental set-up, and we are still working on this method.

d


17

Strobed particles with a long translation d and the DFT result

Strobed particles with a short translation d and the DFT result, showing that it is easy to determine the translation distance.

Another method that we have used, was what one can call a two or three colors imaging velocimetry. The principle is very

simple[43]. We use a color CCD camera and we strobe the image using a very high speed motor (can reach 36000

tour/min) on which we had previously put three colored filters (red, green and blue). The acquired image (see figure 18) is

then splitted in three images, corresponding to the three colors (see figure 19).

figure 19 : Stobed image


18

Figure 20:Image corresponding to the Red color

Thus if one assumes that S1(x,y) is the signal provided by the red particles, S2 (x,y) the signal provided by the bleue

particles. Then we have the relation S2(x,y)=S1(x-h,y-t), where h and t corresponds to the translation effectuated by the

particles. After having binarised our images, we soustracted each image to the other in order to remove both the blue and

green components in the red image, as well as the noisy area. The final images are displayed below:

Figure 21: Image corresponding to the red color and the blue color

Then, when we do the intercorrelation function between the two images, we have:

φ τ ϖ τ ϖ( , ) ( , ). ( , )= + +∑S i j S i jn

1 2 (31)

Where n is the number of particles of the signal S1(x). If one takes into account the previous relations between the two

signals, then one can write:

φ τ ϖ τ ϖ( , ) ( , ). ( , )= + + + +∑S i h j t S i jn

2 2 (32)

The function is now defined as an autocorrelation function, thus we know [42], that an autocorrelation function is maximal

for an argument equal to zero. Thus from equation (32), one finds that, as expected, the maximal is obtained when:

h

t

=

=

τ

ϖ (33)


19

Therefore, knowing t and h we are able to define the translation distance, and with a good camera calibration we are able to

obtain the following results:

Figure 22: Correlation of the blue and red images, the white rectangle indicates the area on which the intercorrelation has

been achieved

Figure 23: Result of the correlation between the two images

When one takes the maximun of the intercorrelation function, one finds:

Figure 24: Extraction of the maximun of the intercorrelation function

As shown, we performed this task only on a very small area because the processing time is very long due the high number

of operations required. But as the previous method, tools like DSP could increase the processing speed and woud able to

perform the treatment over the whole image.

As a matter of fact, at this very point of view of our work we think that the best solution in order to extract the particles

speed using the CCD technology would be to use the second method displayed (Shclieren method) if one is interested by

fast results.

The DFT is also accurate and fast, but we only developed a monodimernsionnal model which will need to be extended to

two dimensions under certain assumptions, moreover in order to be able to use the DFT, the operator needs a powerfull

system in order to hastily sample the image.

h

t

t

h


20

CONCLUSION

We presented through this article a low cost system using two CCD matrix cameras, a standard acquisition card and a

Personnal Computer which enables the operator to get information about the process during the warm-up period and during

the laser cladding process itself. Some of these information are available in real time, thus they can be used in a closed loop

control.

One of the camera is used as a spectral thermometer and provides temperature measurements.

The use of the CCD Technology enables also the extraction of the width the length of the track and of the powder

spary distribution.

We showed that the data provided by our system enable us to improve the control of the cladding process.

The last part is dedicated to differents methods involving CCD sensors and image processing in order to get information to

the speed of the particles. In this part we present a theoritical model on the powder distribution for coaxial delivery sytem

and supply the reader with different algorithms allowing the extraction of the particles speed.

We are now working on a two dimensional extension of our powder distribution model, in order to compare results

obtained with the simulation DFT and the experiments.

We also expect to use some DSP in order to be able to perform the algorithmes on the whole image.

AKNOWLEDGEMENTS

The authors would like to thank Dr C. Dumont and Ph-D student E. Renier who were involved in the software realization.

We hope that technician J.L. Guyot would find our greetings in this paper for proceeding the tracks.


21

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