Reliability of plasma-sprayed coatings

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Reliability of plasma-sprayed coatings: monitoring the plasma spray process and improving
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2013 J. Phys. D: Appl. Phys. 46 224016
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J. Phys. D: Appl. Phys. 46 (2013) 224016 (16pp)
Reliability of plasma-sprayed coatings:
monitoring the plasma spray process and
improving the quality of coatings
P Fauchais, M Vardelle and A Vardelle
Science des Procédés Céramiques et de Traitements de Surface, UMR 7315 CNRS, Université de
Limoges Centre, Européen de la Céramique, 12 rue Atlantis, 87068 Limoges Cedex, France
Received 23 November 2012, in final form 14 January 2013
Published 16 May 2013
Online at
As for every coating technology, the reliability and reproducibility of coatings are essential for
the development of the plasma spraying technology in industrial manufacturing. They mainly
depend on the process reliability, equipment and spray booth maintenance, operator training
and certification, implementation and use of consistent production practices and
standardization of coating testing. This paper deals with the first issue, that is the monitoring
and control of the plasma spray process; it does not tackle the coating characterization and
testing methods. It begins with a short history of coating quality improvement under plasma
spray conditions over the last few decades, details the plasma spray torches used in the
industry, the development of the measurements of in-flight and impacting particle parameters
and then of sensors. It concludes with the process maps that describe the interrelations
between the operating parameters of the spray process, in-flight particle characteristics and
coating properties and with the potential of in situ monitoring of the process by artificial neural
networks and fuzzy logic methods.
(Some figures may appear in colour only in the online journal)
the actual geometry of the part to be covered, must
be performed. It implies that the variations in the
measurements obtained by one person measuring the same
attribute with the same measuring equipment are the
same, or the variations in measurements obtained when
one person takes multiple measurements using the same
instruments and techniques on identical parts are the same.
(ii) Reproducibility is generally defined by the variation
in average measurements obtained when two or more
people inspect the same parts using the same measuring
technique. For example, it applies when the same coating
must be deposited using the same thermal spray process
in two or more spray booths.
(iii) Reliability of a coating is the probability that it adequately
performs its specified function for a specified period of
time under specified environmental conditions.
However, according to Wigren and Johansson [2], these
words are ‘mostly about average and standard deviation versus
drawing requirements’ and, thus, are about the control of the
variability of coatings.
1. Introduction: repeatability, reproducibility and
reliability of coatings
A significant difference between research laboratories and
companies working in the field of plasma spraying is
that the latter are mainly concerned with coating service
properties (wear and corrosion resistance, thermal protection,
etc) and coating repeatability, reproducibility and reliability,
while researchers in laboratories seek to understand the
coating process and the resulting coating microstructure and
Before going into the details of the matter, we may need
to define the meaning of repeatability, reproducibility and
reliability of coatings under thermal spray conditions. Dwivedi
et al [1] defined these terms as follows.
(i) Repeatability consists in repeating a process to achieve
the same coating. For that, measurements of specific
parameters, based on the expected coating properties
(mechanical, thermal, physical, chemical, etc) and
© 2013 IOP Publishing Ltd
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J. Phys. D: Appl. Phys. 46 (2013) 224016
P Fauchais et al
2. Short history of coating quality improvement
under plasma spray conditions
2.1. Beginning
From the 1960s and almost up to the 1980s, the development
of plasma spraying was carried out in an empirical manner.
The way to proceed consisted in (i) varying the operating
spray parameters (arc current, anode nozzle internal diameter
(i.d.), gas flow rates, etc) for a powder with particles of
given morphology and size distribution and (ii) characterizing
the properties of the resulting coating and evaluating its
performance under specific used conditions. This procedure
was repeated until certain standards were obtained and the
operating parameter set was, then, padlocked [3]. This
approach made it possible to develop efficient coatings for
a broad spectrum of applications. It also made it possible
to develop the monitoring of spray parameters, control
of feedstock (particle morphology and size distribution)
and booth-to-booth consistency (equivalency, calibration,
qualification, position of powder feeder relative to the spray
torch, etc).
Figure 1. Percentage in the number of errors observed in a
production unit according to the different parameters: process,
equipment maintenance, masking tools/tape, operator dependent,
operation sheets, NC (numerical control) program, method,
cleaning [2].
As for every coating technology, the reliability and
reproducibility of plasma-sprayed coatings were essential in
getting this technology adopted as soon as it was introduced in
industrial manufacturing. A large body of literature explains
that improving both involves various challenges. Wigren and
Johansson [2] state that the reliability and reproducibility of
plasma-sprayed coatings depend not only on the different
stages of the spray process, i.e. from the preparation of
the surface of the part to be covered to the characterization
and control of coating properties, but also on the equipment
maintenance, operator training and certification and followup of spray operation sheets [2]. As shown in figure 1,
the coating deposition stage that gives rise to the largest
number of works published in the literature represents only
about 13% of the number of errors occurring during coating
manufacturing. In addition, the booth conditions (temperature,
humidity, evacuation of dust, etc) and the layout of the different
components of the spray equipment (robot and spray torch,
mechanical unit where the part to be covered is fixed, powder
feeder position relative to the torch, etc) have to be controlled.
Finally, the procedures and systems used to characterize
and test coating properties should also be standardized and
carefully run. In fact, many measurements depend on the
quality of the coating surface or the preparation of the sample
This paper aims at showing how, by linking the academic
works and industrial needs, through process improvement and
control, it has been possible to improve the manufacturing of
reproducible and reliable plasma-sprayed coatings. It begins
with a short history of the different and successive methods
used to improve coating quality and the description of the
plasma spray process. It continues with a description of the
sensors that can be used in spray booths to ensure the coating
quality and ends with a presentation of some approaches to
monitor the coating properties in situ.
The paper is restricted to process monitoring and control
and does not discuss the coating characterization and testing
methods and results.
2.2. 1980s–1990s
At the end of the 1980s and in the 1990s two new approaches
were developed:
- The development of commercially available computerized
plasma spray systems.
They allow following the
macroscopic parameters of the process in real time:
arc current, mass flow rates of plasma-forming gases
and powder carrier gas, plasma torch cooling-water
temperature and flow rate, water leakage detection, etc. As
the operation of plasma spray guns is sensitive to ageing
(electrode wear, small displacement of the injector and its
partial clogging, etc) procedures had to be defined to take
into account these phenomena. For example, for a given
arc current, a decrease in arc voltage due to electrode
erosion was compensated by an increase in hydrogen
flow rate in order to rise the arc voltage back to that
obtained with a non-eroded anode–nozzle. However, the
development of sensors measuring particle temperatures
and velocities in-flight later showed that such a procedure
was generally not the best and appropriate for keeping the
heating and acceleration of particles identical.
- The plasma jet temperature fields. Some attempts were
also made to measure the gas velocity fields.
- The particle trajectory distributions within the hot jet and
the effect of the powder carrier gas flow rate on these
distributions for given powder characteristics and injector
i.d. and position.
- The particle velocity and temperature distributions at
impact onto the substrate.
- Finally, the substrate and coating temperature evolution
during the spray process.
Some studies began, then, to link the plasma generation and
heat and momentum transfer to particles to the resulting
coating build-up, microstructure and properties [4, 5]. The
J. Phys. D: Appl. Phys. 46 (2013) 224016
P Fauchais et al
importance of controlling the in-flight parameters of the
hot particle, instead of the macroscopic spray parameters,
was demonstrated in research laboratories at that time.
Rather sophisticated measuring devices were then used.
They employed laser Doppler anemometry (LDA) for the
measurement of particle velocities, phase Doppler shift (phase
Doppler particle analysis, PDPA) for the measurement of
their sizes, and fast pyrometers with response time as short
as 100 ns [6–10] for surface temperature measurements and
also charge-coupled device (CCD) cameras for the detection
of the distribution of hot particle trajectories within the plasma
jet [11]. Most of these techniques could not be used in the usual
environment of spray booths. However, they allowed a much
better understanding of coating generation. Thus, they helped
us to shift from a trial-and-error approach to a more scientific
one progressively transforming the plasma spray process from
an art to a science [12].
Over this period much effort has also been made
to standardize the techniques for coating characterization
(metallography and image analysis, material characterization,
void content and network architecture, etc) and also the control
of coating quality (adhesion–cohesion, mechanical properties,
thermal properties, wear resistance, corrosion resistance,
etc), the latter being more targeted towards the service
Figure 2. (a) Schematic of the different steps of the plasma spray
process when the powder is injected radially into the plasma jet, (b)
typical coating [26].
However this implies:
- to establish, for each coating with specific service
properties, the relationships between the macroscopic
spray parameters–sensor parameters–coating thermomechanical properties and service properties;
- to develop new robust and easy-to-use sensors that
incorporate these relationships;
- to develop controllers that have the ability to modify the
input spray variables accordingly.
2.3. From mid-1990s till now: development of sensors
The techniques developed and/or used in laboratories have
been the basis for the development of simpler and robust
sensors able to work in the harsh environment of spray booths
[13, 14]. At the end of the 1990s, a commercially available
system for the monitoring of in-flight particle conditions
(temperature, velocity and diameter), the DPV 2000® System
(Tecnar Automation, Quebec, CN) was developed, based on
the work of Moreau et al [14]. Almost at the same time, Oseir
proposed an imaging system for in-flight particle temperature
and velocity measurements, the Spray Watch®. It was based
on the work of Vattulainen et al [15]. They both allowed the
monitoring of the effect of the operating parameters (gas flow
rates, nozzle internal diameter, electric power level, injection
conditions, particle size distribution and morphology, etc) on
the in-flight parameters of particles. Extensive research on
relationships between in-flight particle parameters and coating
properties, e.g. [15–24], led to a drastic enhancement in the
process understanding and to the improvement of coating
reproducibility and reliability [16, 18]. However, the linkage
to coating properties is still some sort of enigma. We think that
the following points are missing at the moment and should be
3. Plasma spray process
3.1. Spray process parameters
The plasma spray process is schematized in figure 2. Roughly,
the parameters that should be controlled can be categorized
into three areas.
- The high temperatures and velocities of the plasma jet;
they are essentially linked to the plasma gun operating
- Heating and acceleration of the particles injected in the
plasma jet. They are linked, on the one hand, to the
high-energy gas jet temperature, velocity and composition
fields and, on the other hand, to the particle size range,
morphology, specific mass, distributions of injection
velocity, and injection mode into the plasma jet (radial
or axial). These parameters control the distributions of
size, velocity, temperature, melting state and eventually
chemistry of the particles impacting onto the substrate or
the previously deposited layers.
- The phenomena controlling the deposition of the
impacting particles. They depend on the latter parameters
and on the relative movement between the torch and
the substrate and also on the substrate cooling systems.
Programming the robot spray pattern may control this
relative movement.
It must take into account the
orientation of the plasma torch with respect to the substrate
so that the axis of the torch should be as close as possible
- real-time process control to improve process performance
and meet quality requirements;
- close-loop control for direct, real-time monitoring of
process performance via real-time sensing of particle
- corrective action setting of process variables and postprocess examination.
J. Phys. D: Appl. Phys. 46 (2013) 224016
P Fauchais et al
to the normal to the substrate at the deposited spot
location. It has an effect on the thickness of successive
passes and, thus, of coating, and on temperature of
substrate and coating through cooling systems and pass
thickness. It must be underlined that the monitoring
of coating temperature and through-thickness gradient
during the spray process are key parameters to control
the residual stress distribution in coatings. Finally,
the preheating of the substrate before the beginning
of the deposition process allows for the elimination of
adsorbates and condensates from the substrate surface
and controls, and to a great extent, the coating
To reduce such fluctuations that may modify the in-flight
behaviour of particles, Sulzer Metco has developed the socalled Triplex plasma torch working essentially with Ar–He
gas mixtures at power levels up to 60–80 kW. The electrical
energy is distributed through three parallel arcs striking at a
single anode preceded by insulating rings. The generation of
arcs, longer than those obtained with one stick-type cathode
plasma torch, makes it possible to reduce significantly (4–5
times) the percentage of voltage fluctuations [31]. Actually,
if the voltage fluctuations at the anode are similar to the
fluctuations obtained with the one stick-type cathode torch,
the mean arc voltage is higher: around 100–120 V against
about 40 V. The plasmas flow issuing from the Triplex torch
consists of three jets, and has no axial symmetry. It can be fully
characterized only by plasma computer tomography (PCT), as
developed by Landes [31]: the radiation of the plasma jet,
assumed to be stationary, is detected under several directions,
one after the other and distributed over a sector of 180◦ in
a plane perpendicular to the torch axis. The reconstruction
of cross-section images from tomography data requires high
calculation and storage capacity that now customary PCs can
achieve within an acceptable time [32]. As the plasma consists
of three plasma jets, particles can be injected either between
two gas jets taking advantage of the cage effect or within one
plasma jet [33, 34]
Plasma torches operating with electrical power up to
250 kW have been developed to coat big parts, e.g. the Plazjet
torch, with a button-type cathode. It uses up to 250–300 slm
of N2 and 150 slm of H2 injected in the arc chamber with a
vortex injection and a high swirl. Under such torch working
conditions, the powder mass flow rates injected in the gas flow
can reach 15–20 kg h−1 [35].
The third type of plasma torch is the axial torch developed
by Mettech [36]. It is composed of three cathodes and
three anodes operated by three power supplies (total power
ranging from 50 to 150 kW). The feedstock powder is injected
axially between the three plasma jets converging within an
interchangeable nozzle. Hence, the particle residence time in
the hot zones is drastically increased, and the powder injection
gas flow rate is reduced, the converging plasma jets sucking the
particles in the nozzle. The DE is higher than that obtained with
conventional plasma torches. The coatings have porosities
between 3% and 8%; the oxygen content of metal or alloy
coatings ranges between 1% and 5% and coating adhesion is
good (>40–50 MPa). They are mainly used to spray oxides.
3.2. Types of plasma torches used in the industry
The plasma spray torches function on direct current (d.c.).
They produce high-velocity gas jets with temperatures over
8000 K. In principle, such temperatures make it possible to
melt any material, especially ceramics. The part to be covered
is generally located at 10–12 cm downstream of the torch
nozzle exit and subjected to heat flux up to 2 MW m−2 . The
heat transferred to the substrate and the coating must be, thus,
controlled through both the relative movement between the
torch and the substrate (relative velocity and deposited bead
overlapping), and the substrate and coating cooling. Also,
the high temperatures of plasma jets favour the oxidation of
metal powders. To avoid powder oxidation, spraying must
be performed in a controlled atmosphere or soft vacuum
However, air plasma spraying is the most used plasma
spray technique at the industrial level, and the description of
the plasma spray torches in this section will be limited to the
torches used in air.
In the conventional plasma torches, the heat source is
a direct current arc through which non-oxidizing and noncarburizing gases flow. The powder is injected radially to
the plasma jet axis except in the Mettech Axial torch in
which it is injected into the centre of the torch. Most of
the plasma spray torches operate with one cathode working at
power levels below 40–50 kW. The powder flow rate ranges
between 3 and 6 kg h−1 and deposition efficiency (DE) is
around 50%. Argon is mostly used as the primary gas. It
ensures the arc stabilization inside the nozzle and controls
the flow momentum and the transport rate of mass. It is
generally mixed with hydrogen or helium which increases the
energy capacity of the plasma torch and the heat transfer to
particles [25]. A small addition of hydrogen results in an
increase in the arc voltage and in the torch efficiency. However,
it also enhances the movement of the arc root on the anode wall
and, so, brings about an increase in arc voltage fluctuations.
The ratio V /Vm , where Vm is the arc mean voltage and
V is the voltage fluctuation maximum amplitude, can reach
2.5 [27–29]. The plasma torches working with Ar–He gas
mixtures exhibit lower voltage fluctuations with V /Vm of
about 0.2–0.3. With N2 –H2 mixtures, the phenomena are
slightly different but the voltage fluctuation ratio can easily
reach 2 [30].
3.3. Particles in-flight
In conventional plasma torches, the key problem is the radial
injection of powder in the plasma jet [11]. The optimum
trajectory is obtained when the force imparted to the particles
by the hot gas jet is close to the force imparted by the powder
carrier gas at the injector exit. The hot gas force is linked to
the gas momentum density ρv 2 , where ρ is the gas specific
mass and v its velocity. This quantity varies as the particle
penetrates the plasma jet: the gas velocity and temperature
increase from the jet fringes to its centre, but ρ decreases
correspondingly by less than a few tens of per cent. In addition,
J. Phys. D: Appl. Phys. 46 (2013) 224016
P Fauchais et al
the force of particles depends on their injection velocity and
mass. The injection acceleration is imparted to particles, for
a given injector internal diameter, by the carrier gas flow
rate. Because of the collisions between the particles and
injector wall and between particles themselves, as well as the
rather flat gas radial velocity profile inside the injector (gas
Reynolds number >2000), the particles, whatever their size
is, have about the same velocity and, thus, acceleration at the
injector exit [11]. Therefore, their size distribution must be
as narrow as possible to limit the trajectories’ dispersion. For
example, a ratio of 2 between the diameter of the smallest
and biggest particles (narrow distribution) corresponds to a
ratio of 8 of their mass and thus their forces. In addition,
the morphology of particles modifies their mass density and
must also be considered [37]. Whatever the quality of the
powder injector may be part of the injected particles by-pass the
plasma jet and are entrained in its fringes. The sticking of these
particles to the coating under construction can result in defects
in the coating microstructure and must be avoided. Finally, the
particle velocity and temperature distributions must be tailored,
through the spray parameters to achieve their flattening and
cooling on the substrate and then splat formation and layering
to form coatings.
presence or not of condensates and adsorbates. However, this
velocity cannot be measured in spray booths.
A parameter that is very important to monitor or control
is the temperature of the substrate during the preheating stage
and the temperature of the coating surface during the spraying
and cooling stages. It depends strongly on the spray pattern.
The latter is controlled through the overlapping of the sprayed
material beads. The latter depends, on the one hand, on the
pass thickness and heat flux brought by the impacting particles
and plasma and, on the other, on the coating surface undulation
and dimensional tolerances.
4. Sensors that can be used in spray booths
By definition a sensor is a device that measures a physical
quantity and converts it into a signal, which can be read by an
observer or by an instrument. Sensors used in spray processes,
and described below, are all derived from sophisticated devices
used in laboratories to characterize, for example, particles inflight. They are robust devices that can survive in the harsh
atmosphere of spray booths and are relatively simple so as to
limit the adjustments necessary for laboratory devices. They
are used to characterize either particle in-flight or coating under
3.4. Particles at impact and coating formation
4.1. In-flight particles
As the real contacts between splats, which account for 20–
70% of their surface, control, to a large extent, the coating
thermo-mechanical properties, many works have been devoted
to splat formation over the two last decades.The impact of a
single particle on the substrate and the construction of coating
layers involve complex phenomena (see the reviews [38, 39]).
A particle with size of a few tens of micrometres is flattened
on the substrate in a few microseconds and it is at the moment
impossible to follow the flattening process, as the available
cameras are not fast enough. A solution has been found
by photographing the flattening of one particle in a given
time range [40–42]. Varying the time range for different
particles, assumed to be identical at impact that is with the same
diameter, temperature and velocity, a few photographs (less
than ten) of the flattening at different times could be obtained.
Such images combined with the temperature evolution of
the flattening droplets and the observation of the resulting
splats allow a better understanding of the phenomena taking
place during splat formation. The experimental studies of
splat formation have demonstrated the importance of substrate
preheating over the so-called transition temperature to achieve
disc-shaped splats. This transition temperature corresponds to
the evaporation of condensates and adsorbates at the substrate
surface. Dhiman et al [42] gave a review of how the
splat shape depends on the substrate conditions and particle
impact parameters. The recent work of Goutier et al [43, 44]
has shown that the splat formation is linked to the Weber
number where the velocity considered is that of the flattening
liquid material and not that of the impacting particle as it
has often been proposed (see the reviews [38, 39]). This
velocity depends strongly on the substrate roughness, oxide
layer thickness (most substrates are metals or alloys) and the
4.1.1. Types of sensors. A few sensors, light (<1 kg) and
small enough (<400 cm3 ) are fixed on the plasma torch. They
continuously record the measured parameters. So any variation
is instantly detected and can be corrected accordingly. When
the sensor is fixed aside, the torch is driven in front of the
sensor after a given spray period, usually a few tens of minutes.
Then, problems cannot be detected on line and corrected
The hot particles are detectable if the radiation they emit
or scatter is higher than the radiation of the hot gases. Within
plasma jets, the measurement of the radiation emitted by the
hot particles is only possible in the plasma plume where the
plasma temperature is below 6000–7000 K.
Two types of measurements are considered [10] with
either local or large measurement volumes.
The first
type corresponds to a small measurement volume, below
1 mm3 , coupled with relatively high-speed detectors and
electronics with bandwidths in the order of 0.1–1 MHz or
larger. The observation of a single particle is then possible
[10, 45]. A sufficient number (several thousands) of individual
particles must be observed to achieve an adequate statistical
representation of the mean and standard deviations of the
particle temperatures, velocities and diameters recorded. Such
measurements usually require low loaded thermal spray
processes and are often performed with powder mass flow
rates lower than 0.5–1 kg h−1 . It is then assumed that the same
results are obtained under actual spray conditions, which is
true as long as no loading effect occurs. Large measurement
volumes, consisting of an approximately cylindrical chord of
few tens of mm3 , through the spray jet, contain an important
number of particles at a given time and are called ensemble
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P Fauchais et al
measurements, as they do not attempt to distinguish between
individual particles. This chord is preferably oriented in the
plane of the injector so that the measurement is insensitive
to the movement of the spray jet relative to the measurement
volume. Ensemble techniques work equally well for heavily
loaded processes such as HVOF spraying and measurement
times are a few seconds. Ensemble measurement is faster
than local measurement: a few seconds against a few minutes.
Mauer et al [46] have compared local measurement (performed
with DPV 2000®) and ensemble measurement (performed
with Accuraspray-g3®). This comparison was made after
measuring, and then weighting by the local particle flow
rates, the different local mean values of the particle data
(temperatures and velocities) at each DPV2000 grid point
that is contained by the Accuraspray-g3 measurement volume.
They found that the results obtained with both systems were in
good agreement, thus confirming the measurement accuracy
of both. However, as already emphasized by Renouard-Vallet
[47], it is much less useful to know that the mean temperature
of in-flight particles at a given location is 2300 ◦ C (ensemble
measurement), than to know that the temperatures are between
1050 and 4000 ◦ C (local measurement).
Figure 3. (a) Particle Flux Imaging® (PFI-S) image of a running
process (left), (b) PFI-S image with two calculated ellipses
(right) [31].
- The ThermaVizTM of Stratonics Inc., CA, USA, is based
on a CCD camera with 640 × 480 pixels, working at a rate
of 30 Hz [48] with typical exposure times from 5 to 20 µs.
It is a two-colour imaging pyrometer at wavelengths of
625 and 800 nm and makes it possible to also determine
the particle velocities and sizes.
- The Flux Sentinel sensor (Cyber Materials LLC, Boston,
MA, USA) is also a CCD-based sensor and measures
particle temperatures, diameters and velocities. It has,
according to authors [49], the ability to accurately measure
diameter in order to ensure the proper volume-weighted
contributions of individual particles to the molten volume
flux. It senses the entire plasma plume and captures a large
enough ensemble of particles at a rate of about 500–1000
particles per second and quickly determines the plume
4.1.2. Commercial sensors measuring particle temperatures,
velocities and eventually diameters.
- In the DPV 2000 from Tecnar (CN), [14], the velocity is
obtained by measuring the time between the two signals,
which are triggered by a radiating particle passing the
two-slit mask of the optoelectronic sensor head. The
temperature is determined by calculating the ratio of the
energy radiated at two different wavelengths assuming
that the particles are grey body emitters with the same
emissivity at both colour bands. The particle diameter
measurement uses the radiation energy emitted at one
wavelength by the hot particles. It assumes that the
particles are spherical and the emissivity of hot particles
is precisely known at the temperature measured with the
two-colour pyrometer. This second assumption is often
- Contrary to the DPV-2000 the Accuraspray-g3, from
Tecnar (CN), provides ensemble average data of particle
characteristics in a measurement volume 25 mm in length
and 3 mm in diameter. Particle velocities are obtained
from cross-correlation of signals, which are recorded
at two closely spaced locations. The temperatures
are determined by two-colour pyrometry. The system
involves a CCD camera enabling the analysis of the
plume appearance (position, width, intensity) along a line
perpendicular to the particle jet.
- In the Spray Watch, from Oseir, the measurement volume
is adjustable from 18 × 14 × 5 mm3 to 36 × 28 × 30 mm3
[15]. Hot particles are imaged onto a CCD camera sensor
with the aid of a custom-designed dichroic double mirror.
The front and back surfaces of the mirror are designed to
reflect different spectral bands and the corresponding first
and second reflections produce spectral two-colour double
images of single particles onto the CCD sensor.
Whatever may be the sensor used, a prior calibration is
mandatory as well as regular calibrations after a certain time
of use. Velocity measurements have a precision better than
5%. The calibration is trickier for the pyrometer [33] and the
accuracy lower.
4.1.3. Hot particle trajectories distributions.
- The Spray and Deposit Control (SDC® ) [11, 50], based on
the radiation of hot particles, uses either a CCD camera
or a photodiode array where the image of a section of the
plasma jet plume or the flame is focused. A filter with
a 3 nm band pass allows eliminating the most important
part of the plasma plume light. It is possible to record
4 images per second and, as the SDC is fixed on the
plasma torch, the particle trajectories can be continuously
monitored. The SDC also allows following the evolution,
with the operating conditions, of the maximum of the
signal emitted by the hot particles as well as the position
of this maximum relatively to the torch axis.
- The Particle Flux Imaging® (PFI) from Linspray [31, 51],
using a CCD camera, records the plasma jet close to the
torch exit, the particle flux (ensemble measurement) in the
plasma jet plume and also the sprayed spot, figure 3(a).
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P Fauchais et al
A PC reduces the information by finding lines of constant
radiation intensity in the images of the hot plasma jet and
particle flux. These lines can be approximated by ellipses,
as shown in figure 3(b). This system also allows detecting
the possible perturbation of hot gas jet by the carrier gas
when the latter is injected radially to the plasma jet. In
this way, variations in the hot plasma jet as well as in the
particle flux can be detected without a precise knowledge
of physical plasma jet or particle parameters.
- The Sprayview® of Tecnar (CN) uses also a CCD camera
with adapted filters. It allows, when injection is not
optimal, observing phenomena such as the bouncing of
fine particles on the plasma jet and coarse particles flying
all the way through the plasma. The good contrast
between the injected particles and the plasma jet permits
the visualization of the injection cone and the quantitative
characterization of the injection zone, such as cone
width and angle, particle mean velocity, acceleration and
trajectory angle. However, it is not fixed on the spray gun.
individual spray pass thickness during deposition. The
strategy consists of recording the profile of the coating
at the frontier between the new layer and the previous
one using a laser line projected across the pass edge
and captured with a CCD camera.
are independent of coating/substrate nature, surface
roughness or thermal expansion of the coated part. For
on-line tests on cylinders the precision is about 5 µm.
5. On-line monitoring or control of plasma sprayed
5.1. Particle temperatures and velocities measured with
This section summarizes some works carried out with the
sensors presented in the previous sections and shows their
capacity to help one to have a better control of the plasma
spray process.
One of the first results obtained with the DPV 2000 was
the demonstration of the particle temperature and velocity
shifts with the wear of the plasma torch electrodes during a
long-term experiment [59]. A F4-MB plasma gun (Sulzer)
has been operated for more than 50 h, with an average of 2.5
stops/starts per hour. The particle velocity and temperatures
were measured, as shown in figure 4, both under the torch
nominal operating conditions, i.e. at constant arc current
(circles), and after the adjustment of the arc current (squares
and triangles). Figure 4 also presents the evolution of the
electric power dissipated and the net energy. Variations in
the particle state and gun characteristics were significant with
spraying time and demonstrate the necessity to modify the
spray parameters to keep the particle temperature and velocity
at impact as constant as possible. The deposition efficiencies
and coating porosities were compared for different spray gun
conditions yielding a similar input power. The comparison
showed that the same input power obtained by increasing the
arc current or by increasing the hydrogen flow rate resulted in
different coating properties.
Another study showed the effect of the radial injector
angle, hydrogen gas content and arc current on the DE and
microstructure of plasma-sprayed yttria-stabilized zirconia
(YSZ) coatings [60]. Marple et al [61], using the Accuraspray
system, have compared YSZ coatings sprayed with Ar–H2 and
N2 –H2 plasmas. With N2 –H2 plasma gas mixture, higher inflight particle temperatures and lower particle velocities were
produced as compared with Ar–H2 plasmas. The coatings
had similar hardness values; however, Young’s modulus and
thermal diffusivity following heat treatment at 1400 ◦ C were
lower than that of coatings processed with the Ar–H2 gas
mixtures. Tekmen et al [62], using the Accurasprayg3 sensor,
optimized the plasma spraying of cast iron to control the
graphite content in coatings. A wide range of in-flight particle
temperature and velocity values with constant graphite carbon
content was determined. Zhang et al [63] have studied the
effect of particle parameters onto the ionic conduction of
yttria-partially stabilized zirconia (YPSZ) coatings. Wang
et al [64] have shown the possibility to tailor alumina–zirconia
4.2. Measurements linked to coating generation
At the moment, four parameters can be measured:
i. The heat flux brought by the hot gases.
Such a
measurement makes it possible to achieve a better
control of substrate and coating heating during the spray
process. The heat flux, because of the hot gas expansion
downstream of the nozzle exit, decreases rather rapidly
with the spray distance (almost exponentially). For
axisymmetric jets, commercial calorimeters exist, but they
generally can be used for heat fluxes below 5 MW m−2 .
For higher heat fluxes the calorimeters are generally
designed by the users. Such a calorimeter consists of
a central part with a surface between ten and a few
hundred mm2 , high-pressure (up to 3 MPa) water-cooled,
surrounded by a ring calorimeter also water-cooled [52].
One of the difficulties is the interpretation of results as the
calorimeter modifies the free gas flow.
ii. The substrate and coating temperature during preheating, spraying and cooling. They are measured with
infrared (IR) pyrometers [53] with wavelengths over 6 µm
that are less sensitive to the radiation of the plasma, hot
gases and hot particles. For example, such a pyrometer is
fixed on the SDC system. IR thermography is also used
[54] but the thermal imaging cameras are more expensive
than pyrometers.
iii. Stress development during spraying. To determine the
stress formation in situ during spraying, the curvature
of a beam, made of the substrate metal, is continuously
recorded during the various stages of the spray process
(preheating, coating and cooling) as well as the substrate
and coating temperature [10, 52, 55–57].
iv. Coating thickness. It is one of the most important
parameters to monitor and control. At the moment, most
measurements are generally performed after spraying;
they are destructive and time consuming. A novel
approach has recently been developed [58] that enables
the on-line, real time and non-contact measurement of
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Figure 5. First-order process map for air plasma spraying (APS) of
CoNiCrAlY material. Results identify the particle state response for
various torch operating conditions as well as the control vectors
identifying the influence of arc current and secondary gas flow
rate [18].
choice of critical parameters. They controlled the particle state
by varying the critical torch parameters (primary gas flow and
arc current) in a narrow range using yttria (8 wt%)–zirconia
particles with angular shape. The particle states resulting from
averaged individual particle measurements (DPV 2000) were
surprisingly stable with variability in temperature lower than
1% and in velocity lower than 4%. Ensemble approaches
yielded a somewhat higher variability (5% in temperature).
Despite this, the variability in basic coating attributes, such
as thickness and weight, was surprisingly large. Measuring
the particle temperature and velocity also permitted to better
understand the operation mode of new plasma torches such
as the Triplex Pro torch that involves three cathodes and
one anode. With this configuration, the plasma jet can be
considered as three plasma jets flowing aside. Thus, the
particle treatment will differ when they are injected in one
plasma jet or between two of them. Between two jets, the
gas viscosity is lower and the particle penetration easier, while
the injection in one jet requires a higher injection force of
particles because of the higher viscosity of the gas. This
is illustrated in figure 6 from Mauer et al [34] where the
powder injection between two jets is indicated as ‘between
arcs’, while that in the jet is called ‘close to arc’. Three
spray conditions were used. Results presented in figure 6
show that the highest temperatures were obtained with case
1 corresponding to a net power of 30.9 kW with a gas velocity,
vg , of 1250 m s−1 , while similar values were obtained for cases
2 and 3 corresponding to 25.3 kW with vg = 1140 m s−1 and
37.6 kW with vg = 1940 m s−1 , respectively. In the three cases
the injection of the powder between the plasma jets resulted
in higher particle temperatures, phenomenon called by authors
‘the cage effect’.
Zhang and Sampath [68] defined a group of dimensionless
parameters, MI and Re, to represent the in-flight status of
particles. The melting index (MI) describes the molten
state of a given particle by normalizing the measured surface
Figure 4. Evolution of the plasma (a) gun power and (b) net energy,
(c) in-flight particle temperature and (d) velocity during 55 h of
spraying. Nominal operating conditions (circles); constant power
(squares); and constant in-flight particle state (triangles) [59].
coatings with two powder injection ports. Fang et al [65]
have studied the effect of the spray conditions on the YPSZ
particle parameters. Shinoda et al [66] have studied the powder
loading effects for various YSZ powders under atmospheric
d.c. plasma spraying. Statistical temperature distributions of
in-flight particles suggested a rapid decrease in the number
of semi-molten particles above a certain powder-loading rate.
Despite drops in particle temperature and velocity due to the
powder loading effect, the DE tended to increase in some cases.
According to authors the trapping of un-melted particles at
impact, due to a high particle flux, could contribute to the
increase in the DE under these plasma spray conditions.
Such measurements made it possible to draw what
Sampath et al [18] called first-order process maps as illustrated
in figure 5. This figure identifies the particle (CoNiCrAlY, 38–
57 µm) state response for various torch operating conditions
as well as the control vectors identifying the influence of the
secondary gas flow rate and arc current in terms of particle
temperatures and velocities. These quantitative vectors can be
used as feedback control.
From such measurements Srinivasan et al [67] estimated
that a few control protocols exist to monitor the particle
state (predominantly temperature and velocity) with judicious
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distribution, ratio that they called spray stream melting index
(SSMI). The SSMI represents the molten content in the spray
stream. According to authors, this approach provides a reliable
quantitative representation of the molten content in the spray
stream as a whole. The mass flux of molten particles correlates
better with the coating thickness than bulk-average temperature
or light intensity. However, the measurement of the molten
mass flux requires sensors that are capable of sensing particle
states at high rates from across a large portion of the full plasma
plume as the Flux Sentinel sensor does [49]. Only a subset of
the particles in the jet plume is incorporated in the coating:
primarily those that are molten. The characteristics of this
molten volume ensemble, MVE, differ from those of the entire
ensemble, with the MVE characterized by smaller, hotter and
faster particles. Volume weighting eliminates the bias towards
smaller particles inherent with straight number averages.
Figure 6. Volume flow averaged particle temperatures as a function
of carrier gas flow and azimuthal injector position
(TriplexProTM torch, anode–nozzle internal diameter 8 mm, cases
1–3, short injector mount). The azimuthal injector positions are
indicated in the upper legend [34].
5.2. Coating properties monitoring
Despite the small variability observed in the particle state,
substantial variability can be observed in coating properties
[16, 70]. To establish a relationship between the in-flight
particle characteristics measurements and coating properties
other parameters must be recorded during the spray process,
such as the coating mean temperature evolution, stress
distribution evolution and spray pattern, and linked to coating
characteristics such as porosity, phase content, oxide content
and thermo-mechanical properties. The spray pattern, i.e. the
kinematics and geometric parameters of coating formation
such as spray velocity, spray angle and stand off distance,
is an important parameter for coating quality. As explained
by Floristán et al ‘robot trajectory planning for highquality thermal spray coating processes should integrate an
accurate definition and control of process kinematics, but it
should also give rise to an optimal thermal guidance during
deposition’ [71].
The generation of the robot trajectory plays an important
role in thermal spraying to achieve a coating of uniform
thickness with as less undulations as possible, and a perfect
control of the temperature gradients within coatings. The
latter depend on the heat flux brought by the plasma jet and
particles and pass thickness. The use of a robot to control
the spray pattern improves the reliability and reproducibility
of coating properties [72]. It can be programmed either
online or offline. The online programming method is easy
to operate but difficult to use for complex work-pieces and
it does not generally take into account the heat transfer to
substrate. The offline programming technology gives much
better results and can consider the local heat load. Floristán
et al [71], using offline programming, have applied a thermal
load on the substrate surface to model the heat transfer. The
moving hot spot described the programmed spray path and
gave the heat contribution from the plasma and particles. The
velocity between the torch and substrate was considered in the
simulation by the loading time at each node obtained from
the robot-programming tool. The heat flux was simulated as
a Gaussian distribution on the substrate and oriented at each
node according to the torch orientation relative to the substrate
temperature with the particle dwell time and size. The
Reynolds number (Re) of the particle at impact describes its
kinetic state. Figure 7(a) presents a generalized first-order map
based on a large number of process diagnostic results obtained
from multiple materials, and processed through a typical air
plasma spray torch under one or more operating conditions; the
powder injection was optimized for each material so as to have
maximum heat and momentum transfer for each condition.
According to the melting temperature, aluminium is on the
bottom of the plot while molybdenum and tungsten are on top.
Moreover, the surface temperature is not necessarily linked to
particle melting: low thermal conductivity material can present
an important temperature gradient and metal oxidation also
plays a role. The melting percentage of the particle depends
on its size, thermal conductivity, latent heat of fusion and
residence time in the plasma jet. Finally, the study shows
that particle impact velocity is not sufficient to characterize its
flattening. Figure 7(b) shows the individual particle MI and
Re numbers, and average values based on measurements of
particle velocity and temperature for each of the materials. In
the MI–Re- based first-order process map, it is clearly seen that
low-melting point aluminium shows the highest value of MI
(melting state), while tungsten shows the lowest value of MI
despite that it has a much higher temperature in the Tp − vp based first-order process map.
However, as soon as the molten particle flattens onto
the substrate, as demonstrated by Goutier et al [43, 44], the
velocity to be considered is that of the flattening particle which
is linked to the impact velocity but also to the substrate-droplet
wettability, substrate roughness and skewness, evaporation of
adsorbates and condensates. The dimensionless number that
should be used to characterize the flattening process is, thus,
the Weber number calculated with the flattening velocity.
Considering particle temperature distributions, where
particles’ temperatures measured with DPV 2000 are over
and below the melting temperature, Srinivasan and Sampath
[69] have defined the ratio of the sum of the area under
the completely molten peaks and a factor of the area of
the partially molten peak to the total area under the whole
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P Fauchais et al
Figure 7. (a) Tp − vp global process map, (b) MI-Re global process map [68].
to introduce artefacts. The in situ curvature sensor technique
[75], where coatings sprayed on a beam-shaped substrate were
thermally cycled, minimized the source of errors associated
with the multistep/destructive characterization techniques and
provided a means to assess their variability. Vaidya et al
[74] and Dwivedi et al [1] further developed strategies to
identify the process windows in the particle state space to
reliably achieve a specific coating property. In this case, the
second-order map could be established representing different
property contours. For example, figure 8 shows the thermal
conductivity, through-thickness indentation modulus and inplane curvature modulus superimposed on the MI–kinetic
energy space to identify the appropriate process windows for
microstructure tailoring [74]. Valarezo and Sampath [76]
used five different thermal spray processes to spray NiCr
coatings. The particle temperature–velocity window was
explored from low-velocity high-temperature conditions (APS
like) to supersonic-velocity lower temperature conditions
(HVOF like), and conditions in between. The correlation
between particle states and evolving coating stress obtained via
in situ monitoring of coating deposition indicated increment of
compressive stress at high particle kinetic energies, as well as
enhanced strain hardening via peening. The coating hardness,
therefore, showed a strong dependence on the residual stress
evolution. The elastic modulus was found to be strongly
dependent on the coating densification and inter-splat bonding,
whereas the electrical and thermal conductivities were found to
be more sensitive to defects at the inter-splat interfaces (oxides,
interlamellar porosity). The residual stress was also found to
strongly depend on other parameters such as powder feed rate
and spray pattern [77]. Thus, the use of second-order maps
such as that presented in figure 8 will improve the coating
reliability only if these two parameters are kept constant.
Tan et al [78] have used process-mapping strategies
using a novel uniform shell thickness hollow powder
to control the defect microstructure and properties of
thermal barrier coatings (TBCs).
Correlations between
coating properties, microstructure and processing showed the
feasibility to produce highly compliant and low conductivity
TBCs through a combination of optimized feedstock and
at that point of the trajectory [71]. This approach implied that
the heat fluxes brought by the hot gases were first measured at
different positions in the hot jet.
As previously underlined, still no clear relationship exists
between, on the one hand, in-flight particle parameters,
substrate and coating temperature evolution before, during
and after spraying and coating thermo-mechanical properties.
For example, Friis et al [73] found that models based on
particle in-flight properties explained the variations in the
microstructures of plasma-sprayed YSZ coatings as good
as or better than variations in the spray gun parameters
Sampath et al [18] also found that the particle
temperature–velocity space allowed a systematic recognition
of the contribution of the process variables and sensitivities.
Such measurements combined with coating properties offer
a strategy for coating design. They showed that if the
elastic modulus was a reasonable quantitative descriptor of a
coating property, non-linear response might also be considered
for porous coatings, especially when subjected to thermomechanical loading [18]. Vaidya et al [74] and Dwivedi
et al [1] have presented results on the process–structure–
property relations for plasma spraying of YSZ powders with
different morphologies (polyhedral, hollow spherical and solid
spherical) thanks to a critical examination of the different
stages of the spray process, namely the in-flight particle state
and deposit properties, and the interrelations among them.
The first-order process map was the relation between the
in-flight particle state, plasma-forming torch parameters and
feedstock characteristics. Independent control of particle
injection and construction of the first-order process maps
through the use of dimensionless group parameters enabled a
unified assessment of various powder morphologies through
the single first-order process map. From these results a
critical comparison of only the material response was achieved
with second-order maps connecting coating properties to
the particle states. However, they underlined that postspray characterization using metallography, indentation and
thermal conductivity is prone to higher intrinsic variability
associated both with spray conditions and coating preparation,
especially for the destructive techniques, which have potential
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P Fauchais et al
Figure 8. Three different property contours superimposed on the
MI–KE space to identify appropriate process windows which can be
used for microstructure tailoring. W1, W2 and W3 represent three
regimes of coating properties and their relationships to processing
conditions. Such a map provides an opportunity to optimize coating
microstructure based on multifunctional design requirements. K is
the thermal conductivity, Ei is the through thickness indentation
modulus and Ec is the in-plane curvature module [74].
Figure 9. Sensitivity of coating hardness (HV3N ), oxide content
(wt%) and thickness (µm) to the maximum intensity of the radiation
of hot particles and coating temperature during plasma spraying of
NiCuIn particles [81].
temperature. This figure shows that, as soon as the slope of
the iso-values is close to be parallel to one of the axes, the
coating property studied is very sensitive to the variation of
the SDC parameter of the perpendicular axis. The central
zone, surrounded by an ellipse-shaped line, corresponds to
the coating properties expected by the manufacturer. As soon
as the SDC parameters deviate slightly from this zone, the
operator modifies parameters such as the hydrogen flow rate,
the carrier gas flow rate or the cooling airflow rate to keep the
SDC parameters in the optimum working zone. This technique
has reduced the number of parts rejected by more than 90%.
To conclude this section, it is now possible to monitor the
spray parameters using (i) sensors characterizing particles in
flight, substrate and coating temperature and coating residual
stress developed during the spray process, and (ii) robot to
control the spray pattern. It allows for determining an optimum
working zone where the operator can keep the spray parameters
by modifying the parameters of the spray process provided the
spray pattern is not changed. The next step is to control and
not monitor these parameters.
processing conditions. Process maps made it possible to
establish correlations between process parameters, coating
microstructure and properties and so the TBCs’ microstructure
and properties.
Basu et al [79] and Gevelber et al [80] tried developing
real-time control of the plasma spray process. Their work
consisted of measuring the in-flight particle parameters, spray
pattern, including the spray jet position, and adjusting the torch
inputs in order to maintain the particle parameters and spray
pattern at their set point.
Another approach was to define a ‘good working area’, as
proposed by Renault et al [81] for plasma-sprayed CuNiIn (Ni
35 wt%, In 5 wt%) particles (10–45 µm). They used the spray
and deposit control (SDC) system fixed on a commercial d.c.
plasma torch. The parameters measured by the SDC were the
maximum intensity emitted by hot particles, position of this
maximum relatively to the torch axis, spray jet width as well
as the substrate and coating temperature. The powder mass
flow rate, the spray pattern and the relative velocity between
the torch and the substrate were kept constant while the torch
input parameters were varied around the standard conditions
used by SNECMA Co. Using a factorial design, a regression
equation was established between the SDC parameters and
coating hardness, oxide content and thickness. For instance,
figure 9 represents the sensitivity of coating properties to
the maximum radiation intensity of hot particles and coating
5.3. On-line control of coating properties
As shown in the previous section diagnostic tools do not offer
opportunities to tune the operation conditions. To do so [82],
a feedback system involving at least correlations between
each processing parameter and each measured characteristic
is required. A promising approach is artificial intelligence
(AI), based on artificial neural networks (ANN) that have
J. Phys. D: Appl. Phys. 46 (2013) 224016
P Fauchais et al
Figure 11. The ANN optimization process [84].
Figure 10. ANN principle: (a) typical ANN feed forward
architecture, (b) neuron model [83].
proved to be a pertinent tool to predict particle in-flight
characteristics and coating structural attributes from the
knowledge of processing parameters [82]. In the ANN model
the discrimination of complex correlations between the process
input (I) and the process output (O) is a simple, mathematical
operation processed through units called neurons. As shown
in figure 10(a) from Kanta et al [83], the ANN architecture
comprises of an input layer (receiving input data), one or
more hidden layer(s) (connecting input and output layers
representing correlations encoded by the system) and output
layer (i.e. ANN result). The neuron (elementary processor of
the ANN) is represented in figure 10(b), with the input being
the sum of flow coming from neurons connected upstream,
the activation or transfer function making the input nonlinear
and the output resulting from the transformation allowing
supplying the neurons connected downstream [83]. The
strength of a given connection is quantified by a number, which
is termed as a weight. For more details about the way the
weights are tuned, see Guessasma et al [82].
For example Liu et al [84] used this approach to study
plasma-sprayed Al2 O3 –TiO2 (13 wt%) coatings and defined
the ANN as follows: four neurons in the input layer (current
intensity, total mass flow rate, H2 /Ar ratio and air cooling
flow), 50 and 30 neurons in the 1st layer and the 2nd layers,
respectively, forming the so-called hidden layers and three
neurons in the output layer (particle velocity and temperature,
and substrate temperature). The ANN experimental database
Figure 12. Fuzzy logic basis system [85].
was divided into training, test and validation samples, as shown
in figure 11. After 5000 iterations, ANN was stopped. Once
trained with the results of experiments, ANN could predict
the in-flight characteristics of particles and temperature of the
substrate with an error lower than 5%.
For coating manufacturing, one difficulty lies in the fact
that any modification of the macroscopic spray parameters (arc
current, plasma forming gas flow rates) implies modifications
of the powder carrier gas. Kanta et al [83, 85] suggested
that AI could be a suitable approach to predict the operating
parameters required to manufacture coatings with specific
characteristics. They implemented ANN band fuzzy logic
(FL) approaches to predict in-flight particles characteristics
as a function of process parameters. Figure 12 from Kanta
et al [85] represents the FL basis system. The model (figure 12)
is generally implemented in the following three successive
steps, namely fuzzification, rule evaluation (or inference) and
defuzzification [86].
The FL controller was implemented to control and regulate
the processing parameters (arc current intensity, total gas flow
rate, hydrogen percentage) of plasma sprayed alumina–titania
J. Phys. D: Appl. Phys. 46 (2013) 224016
P Fauchais et al
Figure 14. ANN-predicted variation of in-flight particle average
characteristics as a function of deposition yield for several fixed
coating porosities [83].
Figure 13. Effect of hydrogen percentage on in-flight Al2 O3 –TiO2
particle characteristics. Spraying performed with a Sulzer F4-type
plasma torch working with an arc current of 530 A and total plasma
gas mass flow rate of 72.3 g min−1 . The error bars represent the
standard deviation associated with the experimentally determined
average values [85].
many other parameters have to be considered and controlled
to deposit reliable and reproducible coatings.
Since the end of the nineties, sensors, able to work in the
harsh environment of spray booths, have been developed and
commercialized. They make it possible to measure in-flight
particle trajectories, temperatures, velocities and eventually
sizes and shapes. Also, infrared pyrometers or cameras
have been used to measure the substrate and coating surface
temperature evolutions during the preheating, coating and
cooling stages. Other sensors have also been used to study
the development of stresses during these three process stages
and measure Young’s module. However, some parameters
that affect coating formation are difficult, if not impossible,
to control by sensors, such as the substrate preparation, the
modification of the oxide layer (composition, thickness and
roughness) that develops on metallic substrates (more than
90% of sprayed parts) during the preheating stage and particle
Sensors that measure particle parameters can either follow
single particles or ensemble of particles. The former make it
possible to get in-flight particle parameters distribution and not
only a mean value as with the latter. Ensemble measurements
require a few seconds and can be used with the same powder
flow rate as that use to deposit coatings. In contrast statistical
measurements need at least a few minutes and require lower
powder mass flow rates than those used for coating. Particle
temperatures and velocities are not necessarily the parameters
best correlated with coating properties and, thus, some authors
proposed instead to consider the melting index (MI) or the
molten flux measured across the entire plume or the particle
trajectory distributions for which new sensors have been
developed. The use of laser illumination has also helped us to
improve the control of the radial injection of the cold particles
in the plasma jet.
Only sensors, light enough to be fixed on the spray torch,
provide continuous information during the spray process and
allow continuous monitoring of the torch parameters, while
when the sensors are disposed aside the spray torch the particle
in-flight parameters can be measured only from time to time.
All these sensors have resulted in a better understanding of
(Al2 O3 –TiO2 , 13 wt%) particle in-flight parameters [85]. For
example, figure 13 represents the effect of the hydrogen content
of the plasma forming gas on the in-flight particle average
velocity and temperature. The predicted results, both with
ANN and FL, indicated the same tendency. When the hydrogen
mass percentage varied from 0.25 wt% to 1.50 wt%, the inflight particle average velocity and temperature increased by
12% and 6%, respectively. Authors [85] concluded that these
models are powerful techniques that deliver a response within
a very short time, once properly prepared. The ANN model
seems well adapted for the process prediction whereas the FL
model appears more adapted for the process control.
ANN was also implemented to predict the APS process
parameters that have to be used to manufacture Al2 O3 –TiO2
(13 wt%) coatings with the desired structural characteristics
[82, 85]. Typical predictions of ANN are presented in figure 14
with in-flight particle average characteristics as a function of
deposition yield for several fixed coating porosities.
The advantage of ANN over other conventional methods
is that it offers a global optimization and does not consider
prior assumptions on parameter correlations. However,
here also a compromise must be found between a large
representation sample and the noise on the measurements
increasing correlatively.
6. Conclusions
The properties of plasma sprayed coatings depend on many
parameters. They are linked to the torch working conditions,
particle in-flight trajectory, temperature, velocity and diameter
distributions, splat formation and layering, heat fluxes to
the substrate and coating under formation and substrate
preparation. However, as pointed out in the introduction [2]
the process itself represents only about 13% of the number of
errors occurring during coating manufacturing (figure 1) and
J. Phys. D: Appl. Phys. 46 (2013) 224016
P Fauchais et al
the effect of the spray gun working conditions on the particle
in-flight parameters.
‘A good working area’ can be defined by associating
in-flight measurements with measurements performed during
spraying (e.g. coating temperature) and characterization of
coating properties. If, during the spray process, the parameters
measured on-line and characterizing the ‘good working area’
deviate from it, the operator can modify one or a few working
parameters of the spray process to bring back the measured
parameters within the ‘good working area’. Moreover, it now
becomes possible using artificial intelligence based on ANN
and/or fuzzy logic to automatically perform this task.
However, the real on-line control of the spray processes
must still be developed and more works on the flattening and
solidification of particles at impact, especially considering the
actual substrate surface and particle layering are mandatory.
The development of sensors working in spray booths
has helped us to improve the coating reproducibility and
reliability, increase our knowledge of the spray process and
also helped us to validate numerical models. These sensors
could be considered as expensive for industrial use. However,
changes and improvements through in-flight particle control
in industrial spray booths have shown to provide significant
savings in material, energy, labour and hardware and add to
the quality of the product [87].
[12] Moreau C, Bisson J-F, Lima R S and Marple B R 2005
Diagnostics for advanced materials processing by plasma
spraying Pure Appl.Chem. 77 443–62
[13] Li C-J, Wu T, Li C-X and Sun B 2003 Effect of spray particle
trajectory on the measurement signal of particle parameters
based on thermal radiation J. Therm. Spray Technol.
12 80–94
[14] Moreau C, Gougeon P, Lamontagne M, Lacasse V,
Vaudreuil G and Cielo P 1994 On-line control of the plasma
spraying process by monitoring the temperature, velocity
and trajectory of in-flight particles Thermal Spray Industrial
Applications ed C C Berndt and S Sampath (Materials Park,
OH: ASM International) pp 431–7
[15] Vattulainen J, Hämäläinen E, Hernberg R, Vuoristo P and
Mäntylä T 2001 Novel method for in-flight particle
temperature and velocity measurements in plasma spraying
using a single CCD camera J. Therm. Spray Technol.
10 94–104
[16] Srinivasan V, Vaidya A, Streibl T, Friis M and Sampath S 2006
On the reproducibility of air plasma spray process and
control of particle state J. Therm. Spray Technol. 15 739–43
[17] Planche M P, Bolot R and Coddet C 2003 In-flight
characteristics of plasma sprayed alumina particles:
measurements, modeling, and comparison J. Therm. Spray
Technol. 12 101–11
[18] Sampath S, Srinivasan V, Valarezo A, Vaidya A and Streibl T
2009 Sensing, control, and in situ measurement of coating
properties: an integrated approach toward establishing
process-property correlations J. Therm. Spray Technol. 18
[19] Prystay M, Gougeon P and Moreau C 1996 Correlation
between particle temperature and velocity and the structure
of plasma sprayed zirconia coatings Thermal Spray:
Practical Solutions for Engineering Problems ed C C
Berndt (Materials Park, OH: ASM International) pp 517–23
[20] Vardelle M, Vardelle A, Leger A C, Fauchais P and Gobin D
1995 Influence of the particle parameters at impact on splat
formation and solidification in plasma spraying processes
J. Therm. Spray Technol. 4 50–8
[21] Fauchais P, Vardelle M, Vardelle A and Bianchi L 1996
Plasma spray: study of the coating generation Ceram. Int.
22 295–303
[22] Fauchais P, Vardelle M, Vardelle A, Bianchi L and Leger A C
1996 Parameters controlling the generation and properties
of plasma sprayed zirconia coatings Plasma Chem. Plasma
Process. (16) S99–126
[23] Wang P, Yu S C M and Ng H W 2007 Correlation of plasma
sprayed coating deposition efficiency with volume flux
measurements by phase Doppler anemometry (PDA)
Plasma Chem. Plasma Process. 27 311–36
[24] Legoux J-G, Arsenault B, Leblanc L, Bouyer V and Moreau C
2002 Evaluation of four high velocity thermal spray guns
using WC-10% Co-4% Cr cermets J. Therm. Spray Technol.
11 86–94
[25] Roumilhac P, Coudert J-F and Fauchais P 1990 Influence of
the arc chamber design and the surrounding atmosphere on
the characteristics and temperature distribution of Ar–H2
and Ar–He spraying plasma jets Plasma Processing and
Synthesis of Materials ed D Apelian and J Szekely
(Pittsburgh, PA: MRS) vol 190, pp 227–333
[26] Herman H 1988 Plasma-sprayed coatings Sci. Am. 259 112–7
[27] Duan Z and Heberlein J 2002 Arc instabilities in a plasma
spray torch J. Therm. Spray Technol. 11 44–51
[28] Coudert J-F, Rat V and Rigot D 2007 Influence of Helmholtz
oscillations on arc voltage fluctuations in a dc plasma
spraying torch J. Phys. D: Appl. Phys. 40 7357–66
[29] Rat V and Coudert J F 2011 Improvement of plasma spray
torch stability by controlling pressure and voltage dynamic
coupling J. Therm. Spray Technol. 20 28–38
[1] Dwivedi G, Wentz T, Srinivasan S and Nakamura T 2010
Assessing process and coating reliability through
monitoring of process and design relevant coating
properties J. Therm. Spray Technol. 19 695–712
[2] Wigren J and Johansson J 2011 Improving reliability of
thermal spray coatings in production environment
Presented at 3rd Plasma Round Table (Johannesburg, South
Africa, 31 October–4 November 2011)
[3] Friis M and Persson C 2003 Control of thermal spray
processes by means of process maps and process windows
J. Therm. Spray Technol. 12 44–52
[4] Vardelle A, Vardelle M, McPherson R and Fauchais P1980
Thermal Spray Conf. (Nederlands Instituut voor
Lastechniek, The Hague, The Netherlands, 19–23 May)
pp 225–31
[5] Fauchais P, Vardelle A and Dussoubs B 2001 Quo Vadis
thermal spraying? J. Therm. Spray Technol. 10 44–66
[6] Fauchais P, Coudert J F and Vardelle M 1989 Diagnostics in
Thermal Plasma Processing, in Plasma Diagnostics vol 1
ed O Auciello and D L Flamm (New York: Academic)
pp 349–446
[7] Fauchais P et al 1992 Diagnostics of thermal spraying plasma
jets J. Therm. Spray Technol. 1 117–28
[8] Coudert J F, Fauchais P and Vardelle M 2002 Diagnostics of
plasma spray process and derived on-line control High
Temp. Mater. Process. 6 247–65
[9] Fincke J R, Haggard D C and Swank W D 2001 Particle
temperature measurement in the thermal spray process
J. Therm. Spray Technol. 10 255–66
[10] Fauchais P and Vardelle M 2010 Sensors in spray processes
J. Therm. Spray Technol. 19 668–94
[11] Vardelle M, Vardelle A, Fauchais P, Li K-I, Dussoubs B and
Themelis N J 2001 Controlling particle injection in plasma
spraying J. Therm. Spray Technol. 10 267–86
J. Phys. D: Appl. Phys. 46 (2013) 224016
P Fauchais et al
[30] Nogues E, Fauchais P, Vardelle M and Granger P 2007
Relation between the arc-root fluctuations, the cold
boundary layer thickness and the particle thermal treatment
J. Therm. Spray Technol. 16 919–26
[31] Landes K 2006 Diagnostics in plasma spraying techniques
Surf. Coat. Technol. 201 1948–54
[32] Schein J, Richter M, Landes K D, Forster G, Zierhut J and
Dzulkor M 2008 Tomographic investigation of plasma jets
produced by multielectrode plasma torches J. Therm. Spray
Technol. 17 338–43
[33] Mauer G, Vaßen R and Stöver D 2011 Plasma and particle
temperature measurements in thermal spray: approaches
and applications J. Therm. Spray Technol. 20 391–406
[34] Mauer G, Vaßen R, Stöver D, Kirner S, Marqués J-L,
Zimmermann S, Forster G and Schein J 2010 Improving
powder injection in plasma spraying by optical diagnostics
of the plasma and particle characterization Thermal Spray:
Global Solutions fur Future Applications (May 3–5, 2010,
Singapore) DVS-Berichte Vol. 264, DVS Media,
Düsseldorf, Germany, 2010, pp 525–30
[35] Morishita T 1991 Coatings by 250 kW plasma jet spray system
Proc. 2nd Plasma Technik Symp. (Luzern, Switzerland) ed S
Blum-Sandmeier et al (Wohlen, Switzerland: Plasma
Technik) pp 137–45
[36] Moreau C, Gougeon P, Burgess A and Ross D 1995
Characterization of particle flows in an axial injection
plasma torch Advances in Thermal Spray Science &
Technology, Proc. 8th National Thermal Spray Conf.
(Houston, TX) ed C C Berndt and S Sampath (Materials
Park, OH: ASM International) pp 141–7
[37] Fauchais P, Montavon G and Bertrand G 2010 From powders
to thermally sprayed coatings J. Therm. Spray Technol.
19 56–80
[38] Fauchais P, Fukumoto M, Vardelle A and Vardelle M 2004
Knowledge concerning splat formation: an invited review J.
Therm. Spray Technol. 13 337–60
[39] Chandra S and Fauchais P 2009 Formation of solid splats
during thermal spray deposition J. Therm. Spray Technol.
18 148–80
[40] Mehdizadeh N Z, Lamontagne M, Moreau C, Chandra S and
Mostaghimi J 2005 Photographing impact of molten
molybdenum particles in a plasma spray J. Therm. Spray
Technol. 14 354–61
[41] McDonald A, Lamontagne M, Chandra S and Moreau C 2006
Photographing impact of plasma sprayed particles on metal
substrates J. Therm. Spray Technol. 15 708–16
[42] Dhiman R, McDonald A and Chandra S 2007 Predicting splat
morphology in a thermal spray process Surf. Coat. Technol.
201 7789–801
[43] Goutier S, Vardelle M, Labbe J C and Fauchais P 2011
Flattening and cooling of millimeter- and micrometer-sized
alumina drops J. Therm. Spray Technol. 20 59–67
[44] Goutier S, Vardelle M and Fauchais P 2011 Comparison
between micrometer and millimetre sized metal and ceramic
lamellas, for a better understanding of the spray process
Proc. 20th Int. Symp. on Plasma Chemistry (Philadelphia,
PA, July 24–29) 201 and
[45] Moreau C, Bisson J-F, Lima R S and Marple B R 2005
Diagnostics for advanced materials processing by plasma
spraying Pure Appl. Chem. 77 443–62
[46] Mauer G, Vaßen R and Stöver D 2007 Comparison and
applications of DPV-2000 and Accuraspray-g3 diagnostic
systems J. Therm. Spray Technol. 16 414–24
[47] Renouard-Vallet G 2004 Elaboration by plasma spraying of
dense and thin (a few tens of micro meters) yttria stabilized
zirconia electrolytes for SOFCs PhD Thesis University of
Limoges France (in French)
[48] Craig J E, Parker R A, Lee D Y, Wakeman T, Heberlein J and
Guru D 2003 Particle Temperature and Velocity
Measurements by Two-Wavelength Streak Imaging, Thermal
Spray 2003: Advancing the Science and Applying the
Technology vol 2 ed B R Marple and C Moreau (Orlando,
FL, 5–8 May 2003) (Materials Park, OH: ASM
International) pp 1107–12
Wroblewski D, Reimann G, Tuttle M, Radgowski D,
Cannamela M, Basu S N and Gevelber M 2010 Sensor
issues and requirements for developing real-time control for
plasma spray deposition J. Therm. Spray Technol.
19 723–35
Vardelle M, Bossoutrot C, Renault Th, Braillard F and
Hoffmann H 2004 Dispositif et procédé de contrôle d’une
projection thermique, SNECMA Services—SPCTS
laboratory, published 03/03/2004, Patent no
EP:1 340 578 A3
Zierhut J, Landes K, Waas C, Kutscher D, Heinrich P
and Krömmer W 1999 Particle flux imaging ITSC99
Proc. ed E Lugsheider (Düsseldorf, Germany: DVS)
pp 340–4
Hugot F, Patru J, Fauchais P and Bianchi L 2007 Modelling of
a substrate thermo-mechanical behavior during plasma
spraying J. Mater. Process. Technol. 190 317–23
Vardelle M, Renault T and Fauchais P 2002 Choice of an IR
pyrometer to measure the surface temperature of a coating
during its formation in air plasma spraying High Temp.
Mater. Process. 6 469–90
Weisheng X, Zhang H, Wang G and Yang Y 2009 A novel
integrated temperature investigation approach of sprayed
coatings during APS process J. Mater. Process. Technol.
209 2897–906
Sampath S and Matejicek J 2002 Method and apparatus
for determining process-induced stresses and elastic
modulus of coatings by in situ measurement US Patent
Matejicek J and Sampath S 2003 In situ measurement
of residual stresses and elastic moduli in thermal
sprayed coatings: I. Apparatus and analysis Acta Mater.
51 863–72
Matejicek J, Sampath S, Gilmore D and Neiser R 2003
In situ measurement of residual stresses and elastic
moduli in thermal sprayed coatings: II. Processing
effects on properties of Mo coatings Acta Mater.
51 873–85
Nadeau A, Pouliot L, Nadeau F, Blain J, Berube S A,
Moreau C and Lamontagne M 2006 A new approach to
online thickness measurement of thermal spray coatings
J. Therm. Spray Technol. 15 744–9
Leblanc L and Moreau C 2002 The long-term stability of
plasma spraying J. Therm. Spray Technol. 11 380–6
Bisson J F, Moreau C, Dorfman M, Dambra C and Mallon J
2005 Influence of hydrogen on the microstructure of
plasma-sprayed yttria-stabilized zirconia coatings J. Therm.
Spray Technol. 14 85–90
Marple B R, Lima R S, Xie L and Dorfman M R 2007
Yttria-stabilized zirconia thermal barriers sprayed using
N2 –H2 and Ar–H2 plasmas: influence of processing and
heat treatment on coating properties J. Therm. Spray
Technol. 16 791–7
Tekmen C, Iwata K, Tsunekawa Y and Okumiya M 2009
Controlling graphite content in plasma sprayed cast iron
coatings via in-flight particle diagnostic J. Mater. Process.
Technol. 209 5417–22
Zhang C, Li C-J, Liao H, Planche M-P, Li C-X and Coddet C
2008 Effect of in-flight particle velocity on the performance
of plasma-sprayed YSZ electrolyte coating for solid oxide
fuel cells Surf. Coat. Technol. 202 2654–60
Wang P, Yu S C M and Ng H W 2004 Particle velocities, sizes
and flux distribution in plasma spray with two powder
injection ports Mater. Sci. Eng. A 383 122–36
J. Phys. D: Appl. Phys. 46 (2013) 224016
P Fauchais et al
[77] Shinoda K, Colmenares-Angulo J, Valarezo A and Sampath S
2012 Effect of deposition rate on the stress evolution of
plasma-sprayed yttria-stabilized zirconia J. Therm. Spray
Technol. 21 1224–33
[78] Tan Y, Srinivasan V, Nakamura T, Sampath S, Bertrand P and
Bertrand G 2012 Optimizing compliance and thermal
conductivity of plasma sprayed thermal barrier coatings via
controlled powders and processing strategies J. Therm.
Spray Technol. 21 950–62
[79] Basu S N et al 2003 Plasma sprayed coatings with engineered
microstructures Advancing the Science and Applying the
Technology ed B R Marple and C Moreau (Materials Park,
OH: ASM International)
[80] Gevelber M, Cui C, Vattiat B, Fieldman Z, Wroblewski D and
Basu S 2005 Real time control for plasma spray: sensor
issues, torch nonlinearites, and control of coating thickness,
in ITSC 2005 proc., (ed.) E Lugsheider (pub.) DVS,
Düsseldorf, Germany, e-proc.
[81] Renault T, Vardelle M, Fauchais P, Hoffmann H and
Braillard F 2001 Thermal Spray 2001: New surfaces for a
new Millenium ed C C Berndt et al (Materials Park, OH:
ASM Internatioal) pp 743–9
[82] Guessasma S, Montavon G and Coddet C 2004 Neural
computation to predict in-flight particle characteristic
dependences from processing parameters in the APS
process J. Therm. Spray Technol. 13 570–85
[83] Kanta A-F, Montavon G, Planche M-P and Coddet C 2009
In-flight and upon impact particle characteristics
modelling in plasma spray process Surf. Coat. Technol.
203 3361–9
[84] Liu T, Planche M-P, Deng S, Montavon G and Kanta A-F 2012
Spray operating parameters optimization based on artificial
intelligence during plasma process Thermal Spray 2012:
Proc. ed R S Lima et al (Materials Park, OH: ASM
International) pp 562–7
[85] Kanta A-F, Montavon G, Vardelle M, Planche M-P,
Berndt C C and Coddet C 2008 Artificial neural networks
vs. fuzzy logic: simple tools to predict and control complex
processes—application to plasma spray processes J. Therm.
Spray Technol. 17 365–76
[86] Ogaji S O T, Marinai L, Sampath S, Singh R and Prober S D
2005 Gas-turbine fault diagnostics: a fuzzy-logic approach
Appl. Energy 82 81–9
[87] VandenBergh Mo 2009 Sprayed particle diagnostics Spraytime
16 9–12
[65] Fang J C, Xu W J, Zhao Z Y and Zeng H P 2007 In-flight
behaviors of ZrO2 particle in plasma spraying Surf. Coat.
Technol. 201 5671–5
[66] Shinoda K, Tan Y and Sampath S 2010 Powder loading effects
of yttria-stabilized zirconia in atmospheric dc plasma
spraying Plasma Chem. Plasma Process. 30 761–78
[67] Srinivasan V, Vaidya A, Streibl T, Friis M and Sampath S
2006 On the reproducibility of air plasma spray process
and control of particle state J. Therm. Spray Technol.
15 739–43
[68] Zhang W and Sampath S 2009 A universal method for
representation of in-flight particle characteristics in thermal
spray processes J. Therm. Spray Technol. 18 23–34
[69] Srinivasan V and Sampath S 2010 Estimation of molten
content of the spray stream from analysis of experimental
particle diagnostics J. Therm. Spray Technol. 19 476–83
[70] Sampath S and Jiang X 2001 Splat formation and
microstructure development during plasma spraying:
deposition temperature effects Mater. Sci. Eng. A
304–306 144–50
[71] Floristán M, Montesinos J A, Garcı́a-Marı́n J A, Killinger A
and Gadow R 2012 Robot Trajectory Planning for High
Quality Thermal Spray Coating Processes on Complex
Shaped Components, in ITSC 2012 (Materials Park, OH:
ASM International) e-proc.
[72] Deng S H , ZhenHua Cai, DanDan Fang, HanLin Liao and
Ghislain Montavon 2012 Application of robot offline
programming in thermal spraying Surf. Coat. Technol.
206 3875–82
[73] Friis M, Persson C and Wigren J 2001 Influence of particle
in-flight characteristics on the microstructure of
atmospheric plasma sprayed yttria stabilized ZrO2 Surf.
Coat. Technol. 141 115–27
[74] Vaidya A, Srinivasan V, Streibl T, Friis M, Chi W and
Sampath S 2008 Process maps for plasma spraying of
yttria-stabilized zirconia: an integrated approach to
design, optimization and reliability Mater. Sci. Eng. A
497 239–53
[75] Liu Y J, Nakamura T, Dwivedi G, Valarezo A and Sampath S
2008 Anelastic behavior of plasma-sprayed zirconia
coatings J. Am. Ceram. Soc. 91 4036–43
[76] Valarezo A and Sampath S 2011 An integrated assessment of
process-microstructure-property relationships for
thermal-sprayed NiCr coatings J. Therm. Spray Technol.
20 1244–58
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