Insérer ici le titre de votre Utilisation d’un diagnostic en

ligne pour le pilotage de

Procédé plasma thermique 3-5 Juin 2009

interventionligne pour le pilotage de propriétés de dépôts

M. VardelleSPCTS (UMR 6638) Univ. of Limoges, France

Thermal Spray ProcessThermal Spray Process

� Production cost

Besides coating properties, reliability andreproducibility of thermal spray coatings

are critical to:

� Coatings as design elements in extreme service conditions

Reproducibility => gas leakage, electrode ornozzle erosion … → must be checked on line

Coating properties and structure Coating properties and structure ����������������

Macroscopic spray parameters Macroscopic spray parameters

1. Substrate preparation

2. Torch working parameters + injector position

+ carrier gas flow rate

3. Kinematics torch/substrate 3. Kinematics torch/substrate

4. Substrate and coating mean temperature

evolution before, during and after spraying

5. Elimination of dust redeposited between

successive passes (3+4)

Monitoring of spray parameters

Carrier gas leakage, injector clogging… not

detected by monitoring macroscopic parameters

Detected by monitoring microscopic parameters

(T , v , fluxes of hot and cold particles, d ?) (Tp, vp, fluxes of hot and cold particles, dp?)

If sensor fixed on the torch or the torch regularly

positioned in front of the sensor during spraying

Monitoring of spray parameters

Microscopic ones: -Tp, vp, dp

slit

powderinjection

plasmajet

sensormask

particleflow

photographic credit: Oseir

-hot particles flux

slitmask

time [ms]

sig

nal [a

.u.]

995 nm

790 nm

CCD cameraand filters

Precision of hot particles measurements

Velocity: 5%

- Temperature: 20% + critical problem of calibration

(at least every two months!) + optical properties.

Minimum detectable temperature => sensor Minimum detectable temperature => sensor

and signal amplifier: between 1100 and 1400 °°°°C.

- Diameter: very poor precision ~ 30%

- Particles flux: reliable

=> excellent for comparative measurements

⇒ Control coating adhesion / cohesion + residual

stress distribution

- Pyrometer wavelength range => absorption

effect of surrounding atmosphere + reflected hot

Monitoring substrate and coating temperatures

effect of surrounding atmosphere + reflected hot

gases emission (i.e., by the substrate or the

surrounding)

- Infrared Thermography camera can also be used

SDC: Substrate and coating temperature control

Experimental SetExperimental Set--up for residual stresses measurementup for residual stresses measurement

LVDT

Torch movement

TorchImaging

axis

Substrate Particle spray jet

SDCPyrometer

Data acquisition and treatment

Water cooled-shield

SDC

SDC: stress control

1,2

1,6

2

2,4D

isp

lacem

en

t (m

m)

150

200

250

300

Dep

osit

tem

pera

ture

(°C

)

00

0,4

0,8

1,2

0 50 100 150 200 250 300

time (s)

Dis

pla

cem

en

t (m

m)

50

100

150

Dep

osit

tem

pera

ture

(

SDC: Residual stresses according to target surface temperature

100

200

300

400S

tres

ses

(MP

a)

Quenching stress

Residual stress

1

2

543

-300

-200

-100

0

∆∆∆∆T (°C)

Str

esse

s (M

Pa)

Thermal stress

Residual stress

10050 300250200150T ref.

Example SDC : hot particles flux T(substrate+coating)

Particle jet

Fluxmetry

Data

Acquis.PC

Torch

MovingSubstrate

Pyrometry

Fluxmetry

fixed on the torch

SDC measurementsSDC measurementsusing radiation from hot particlesusing radiation from hot particles

• D : divergence = width at 1/e.

• Iray : hot particle radiation intensity.

• Y : position from the torch axis of Iray => particle mean

3 parameters are continuously measured :

-20 -15 -10 -5 0 5 10 15 20 25 30

Radial distance from the torch axis (mm)

Iray

Y

D

• Y : position from the torch axis of Iray => particle mean

trajectory.

schematic trajectories

SDC: influence of carrier gas flow rate

10000

light intensity [a.u.]

4.5 SLPM

6 SLPM

5000

0

-10 0 10 20

particle jet radius [mm]

8 SLPM

6 SLPM4 SLPM

2.5 SLPM

Measurement

direction

PowderPowder CuNiIn +10 CuNiIn +10 ––45µm45µm

powder mass flow rate: 1.5kgpowder mass flow rate: 1.5kg

Coating analysisCoating analysis

An exemple of onAn exemple of on--line control with SDCline control with SDC

•• hardness (Vickers, load 3N)hardness (Vickers, load 3N)

•• oxide content (O.M. image analysis)oxide content (O.M. image analysis)

•• coating thickness for the same number of passes coating thickness for the same number of passes

and same torch/substrate velocity and geometry and same torch/substrate velocity and geometry

experiments Argonflow rate

HydrogenFlow rate

Current intensity

Carrier gasflow rate

Standard 0 0 0 0

1 -1 -1 -1 -1

2 1 -1 -1 -1

3 -1 1 -1 -1

4 1 1 -1 -1

5 -1 -1 1 -1

4 input parameters

Values +/- 5%,

Experimental procedure Experimental procedure

5 -1 -1 1 -1

6 1 -1 1 -1

7 -1 1 1 -1

8 1 1 1 -1

9 -1 -1 -1 1

10 1 -1 -1 1

11 -1 1 -1 1

12 1 1 -1 1

13 -1 -1 1 1

14 1 -1 1 1

15 -1 1 1 1

16 1 1 1 1

Values +/- 5%, 10%, 15%, 20%, from the standard values

Sensitivity of SDC measured parameters to process operating Sensitivity of SDC measured parameters to process operating

conditionsconditions

Maximum intensity Mean trajectory position

15

20

Carr

ier

gas

0,4

0,5R

2 = 90.7 %

Ar

* C

arr

ier

gas

Cu

rren

t in

ten

sity

-30

-25

-20

-15

-10

-5

0

5

10

15

Process macroscopic input parameters

R2 = 99.1 %

Carr

ier

gas

Ar

* H

2

Ar

H2

Ar

* C

arr

ier

gas

Cu

rren

t in

ten

sity

-0,2

-0,1

0

0,1

0,2

0,3

0,4

Process macroscopic input parameters

Carr

ier

gas

I

Ar

* C

arr

ier

gas

Ar H2

Cu

rren

t in

ten

sity

Divergence Coating temperature

0,5R2 = 94.7 %

35

40R2 = 95.9 %

Sensitivity of SDC measured parameters to process operating Sensitivity of SDC measured parameters to process operating

conditionsconditions

-0,7

-0,5

-0,3

-0,1

0,1

0,3

Process macroscopic input parameters

R2 = 94.7 %

Ca

rrie

r g

as

Ar

* H

2

Ar

H2

Cu

rre

nt

inte

ns

ity

Ar

* C

urr

en

t

Ar

* C

arr

ier

ga

s

I *

Ca

rrie

r g

as

-10

-5

0

5

10

15

20

25

30

Process macroscopic input parameters

Carr

ier

gas

Ar

* H

2

H2

Cu

rren

t

inte

nsit

y

Oxide Content Coating Hardness

0,4

0,6

0,8

1

1,2

Ca

rrie

r

ga

s

H2

Ar

* C

arr

ier

ga

s

I *

Ca

rrie

r

ga

s

* C

arr

ier

ga

sOxide

3,2

5,2

7,2 R2 = 68 %

I *

Carr

ier

gas

* C

arr

ier

Sensitivity of SDC measured parameters to process operating Sensitivity of SDC measured parameters to process operating

conditionsconditions

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

Process macroscopic input parametersR2 = 72 %

Ca

rrie

r

Ar

* H

2

Ar

H

Cu

rre

nt

inte

nsity

H2 *

I

Ar

* I

Ar

* C

arr

ier

ga

s

I *

Ca

rrie

r

H2 *

Ca

rrie

r g

as

-4,8

-2,8

-0,8

1,2

Process macroscopic input parameters

Carr

ier

gas

Ar

* H

2

Ar

H2

Curr

ent

inte

nsity

H2 *

I

Ar

* I

Ar

* C

arr

ier

gas

I *

Carr

ier

gas

H2 *

Carr

ier

The evolution of : The evolution of :

-- the maximum intensitythe maximum intensity

-- the mean trajectory positionthe mean trajectory position

-- the divergence of the particle spray jet the divergence of the particle spray jet

are represented are represented versus the mean coating temperatureversus the mean coating temperature

Definition of area of good working parametersDefinition of area of good working parameters

are represented are represented versus the mean coating temperatureversus the mean coating temperature

On these graphs are plotted according to the On these graphs are plotted according to the correlations obtained with the factorial design, correlations obtained with the factorial design, the the lineslines corresponding to different corresponding to different values of oxide values of oxide content, hardness, and thicknesscontent, hardness, and thickness

The area of good working conditions is defined by The area of good working conditions is defined by the pink spotthe pink spot

Sensitivity of SDC parameters to Sensitivity of SDC parameters to coating oxide content, hardness, thicknesscoating oxide content, hardness, thickness

110

130

150

170

190M

ax

imu

m in

ten

sit

y (

ua

)

Exp. 5%

Exp. 10%

Exp. 15%

Exp. 20%

5%

4%

170HV 210HV 250HV 290HV

Sensitivity of coatingwt% oxide, hardness,thickness to SDC

10

30

50

70

90

110

150 200 250 300 350Coating temperature (°C)

Ma

xim

um

in

ten

sit

y (

ua

)

3%

2%

130HV

140µm 180µm

220µm

100µm

thickness to SDCparameters

0

0,5

1

1,5

2M

ean

tra

j. p

osit

ion

(m

m)

Exp. 5%

Exp. 10%

Exp. 15%

Exp. 20%130HV

3%

4% 5%170H

V

210HV 250HV

Sensitivity of SDC parameters to Sensitivity of SDC parameters to coating oxide content, hardness, thicknesscoating oxide content, hardness, thickness

Sensitivity of coatingwt% oxide, hardness,thickness to SDC

-3,5

-3

-2,5

-2

-1,5

-1

-0,5

150 200 250 300 350

Coating temperature (°C)

Me

an

tra

j. p

osit

ion

(m

m)

2%

100µm 140µm 180µm

220µm

thickness to SDCparameters

7,0

8,0

9,0

10,0d

ive

rge

nc

e (

mm

)Exp. 5%

Exp. 10%

Exp. 15 %

Exp. 20%

5%210HV

250HV

220µm

260µm

290HV

Sensitivity of SDC parameters to Sensitivity of SDC parameters to coating oxide content, hardness, thicknesscoating oxide content, hardness, thickness Sensitivity of coating

wt% oxide, hardness,thickness to SDCparameters

2,0

3,0

4,0

5,0

6,0

150 200 250 300 350

Coating temperature (°C)

div

erg

en

ce

(m

m)

4%

3%

2%

130HV

170HV

100µ

140µm

180µm

To improve the reliability of thermal sprayed coatings:

……Photometry of particle jet

+

SDC

+

Pyrometry on coating surface

= process on-line control

Factorial design shows that:Factorial design shows that:

•• SDC parameters are SDC parameters are very sensitivevery sensitive to spray to spray

input parametersinput parameters

•• SDC parameters evolve with Hardness, oxide SDC parameters evolve with Hardness, oxide

content, deposit thickness content, deposit thickness content, deposit thickness content, deposit thickness

•• DivergenceDivergence is a very interesting parameter to is a very interesting parameter to

monitor oxide content and coating thickness monitor oxide content and coating thickness

Strategy of onStrategy of on--line controlline control

Measured

on-line

parameters

(SDC)

Coating

properties

•Pporosity

•Ooxide

wt%

Service

properties

�Friction

EXPERT

SYSTEM

DATA

BANKDATA

BANK

Input

parameters

(process)•Ggas flow rate

HH2 vol %

DATA

BANKFor each

material/substrate couple

(SDC)

•-

Maximum.

intensity-

Position traj.-

Divergence

•-Substrate

temperature

wt%•Hhardness

Deposition

efficiency….

�Friction

�Thermal

barrier

BANKBANK•HH2 vol %

•Ccarrier gas

flow rate

Main difficulty: most parameters are cross-linked

On-line

control

Systéme expert basé sur des méthodes d’intelligence artificielle

2 méthodes:

- Réseaux de neurones artificiels (RNA): prédiction des paramétres opératoires pour des conditions données

- Logique Flou (LF) : definition d’une stratégie de régulation des paramétres opératoires en tenant compte des instabilitées…

Abdoul Fatah KANTA (LERMPS)

Abdoul Fatah KANTA

ConclusionsConclusions

Pour évoluer vers un contrôle en boucle, lesinstallations de projections doivent être adaptées pour pouvoir relier les capteurs de mesure à leurs systémes de commande.

Un contrôle en ligne simple peut être mise en place à

partir d’une stratégie définie à partir d’un plan

Conclusions

De la mesure vers un contrôle en boucle fermée:

partir d’une stratégie définie à partir d’un plan

d’expérience effectué pour une application donnée.

���� Relations caractéristiques des dépôts en fonction

des paramétres mesurés du diagnostic

���� Relation paramétres mesurés du diagnostic en

fonction des paramétres opératoires

Un systéme dynamique basé sur des méthodes

d’intelligence artificielle peut jouer le rôle d’interface

entre les capteurs et le systéme de commande du

procédé. Une banque de donnée est necessaire pour

le former et l’optimiser.

Conclusions

De la mesure vers un contrôle en boucle fermée:

le former et l’optimiser.

L’intelligence artificielle (AI) basée sur des réseaux de

neurones (ANN) est un outil pertinent pour la

prédiction des caractéristiques des particules en vol

et les propriétés des dépôts.

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Thank you

Examples of spray process monitoring

• Comparison of different HVOF guns and plasma guns

spraying cermets

• Long term stability (due to DC torch electrode erosion)

•Optimization of agglomerated nano particles spraying..

• Many works necessary to correlate coating

thermo-mechanical properties to in-flight particles

characteristics + substrate and coating temperature

evolution during spraying.

-

Spray jet (fluxmetry, photometry, time of flight): most Spray jet (fluxmetry, photometry, time of flight): most

cases cases ensemble measurements ((observation of an observation of an

ensemble of particles simultaneously):ensemble of particles simultaneously):

Tecnar, Control Vision, Oseir, Stratonics, Lindspray Tecnar, Control Vision, Oseir, Stratonics, Lindspray

Industrial sensors for hot particles monitoring

Tecnar, Control Vision, Oseir, Stratonics, Lindspray Tecnar, Control Vision, Oseir, Stratonics, Lindspray

(Particle Flux Imaging, (Particle Flux Imaging, Particle Shape Imaging) ……

In certain casesIn certain cases individual particle (Tindividual particle (Tpp, v, vpp) measurements) measurements

Abdoul Fatah KANTA