Controlling particle state is important to not only achieve the required microstructure and properties in coatings but also to clearly isolate and understand the role of other clusters of variables (such as the various substrate and deposition conditions) on the aforementioned attributes. This is important to design coatings for high performance applications and in the ongoing efforts towards achieving prime reliance. This study examines the variabilities in particle state and explores a few strategies to control them for improved reproducibility with the aid of in-flight particle and plume sensors. The particle state can be controlled by controlling the torch parameters or by directly controlling the particle state itself via feedback from particle and plume sensors such as DPV2000 & TDS. There exist at least a few control protocols to control the particle state (predominantly temperature and velocity) with judicious choice of critical parameters. In the present case the particle state has been controlled by varying the critical torch parameters in a narrow range using 8% YSZ of angular morphology (fused and crushed) with 10-75 microns size distributions in conjunction with a N 2 -H 2 laminar (non-swirl) plasma. Two important results emerge. (1) The particle state resulting from averaged individual particle measurements (DPV 2000) is surprisingly stable with variabilities in T < 1% and variability in V of < 4%. Ensemble approaches yield a somewhat higher variability (5%). In spite of this the variability in basic coating attributes such as a thickness and weight is surprisingly large. (2) Applying a much simpler control strategy to only control the particle injection and hence the particle trajectory results in reduced variabilities in coating attributes.

Previous investigations of thermal spraying processes have shown that the relations between process parameters and the objective measurements are very complex. An approved approach to control complex processes is the application of Neural Networks (NN). Thus, Neural Networks have been designed to control the process of APS and HVOF spraying. Feed forward Neural Networks (Multi Layer Perceptron, MLP) are used. They are able to control a process. The way to train the Neural Network is to conduct as many experiments as possible, this is the major difficulty for the industrial use of Neural Networks. To save time and money DoE (Design of Experiments) is used to create an optimal experimental plan for the training. For testing the implementation of Neural Networks coatings are sprayed with APS, using DoE. The Neural Networks are combined with the particle flux imaging (PFI) tool. In future this combination will be able to provide an open loop control for thermal spray processes. The Neural Networks will be integrated with the software of the PFI-unit in order to create an easy to handle and affordable process control device. First experiments have been done with the APS process by spraying ZrO 2 onto steel substrates. Afterwards the porosity of the coating was correlated to the recorded images and to the process parameters.

The number of parameters influencing the plasma spraying process is very high. Only a part of these parameters can be controlled online; some of them such as gas flows, current, voltage and spraying distance can be controlled easily, others such as particle temperature and velocity can only be controlled with substantially higher effort. As differences from parameter values preinstalled or given at the start of the process, the noise factors affect the coating properties in different ways and show big effects on the coating quality. Nevertheless there is only little knowledge about the significance of several noise factors and about the influence of small process parameter fluctuations on the coating properties. Because some of these noise factors such as plasma torch degradation cannot be avoided, the aim of this work is to determine the sets of coating parameters, where the influence of noise factors is minimized. This should be achieved by using online diagnostic tools, that afford the observation of fast and easy controllable process characteristics. On the other hand process errors shall be identified in an early process stage using appropriate diagnostic methods.

Over the last decade there has been an explosion in terms of available tools for sensing the particle spray stream during thermal spray processes. This has led to considerable enhancement in our understanding of process reproducibility and process reliability. However, in spite of these advances, the linkage to coating properties has continued to be an enigma. This is partially due to the complex nature of the build-up process and the associated issues with measuring properties of these complex coatings. In this paper, we present an integrated strategy, one that combines process sensing, with process modeling and extracting coating properties in situ through the development of robust and advanced curvature based techniques. These techniques allow estimation of coating modulus, residual stress and non-linear response of thermal sprayed ceramic coatings all within minutes of the deposition process. Finally, the integrated strategy examines the role of process maps for control of the spray stream as well as design of thermal spray coatings. Examples of such studies for both MCrAlY and YSZ coatings will be presented.

In the past ten years, significant progress has been made in the field of advanced sensors for particle and spray plume characterization. However, there are very few commercially available technologies for online characterization of the as-deposited coatings. In particular, coating thickness is one of the most important parameter to monitor and control. Current methods such as destructive tests or direct mechanical measurements can cause significant production downtime. This paper presents a novel approach that enables online, real-time and non-contact measurement of individual spray pass thickness during deposition. Micron-level resolution was achieved on various coatings and substrate materials. The precision has been shown to be independent from surface roughness or thermal expansion. Results obtained on typical HVOF and plasma sprayed coatings are presented. Finally, current fields of application, technical limitations and future developments are discussed.

Reproducibility of plasma spraying process and resulting deposits is one of the most essential requirements for its application in industrial environment. In most cases, only controlling process input parameters does not suffice for achieving stability and reproducibility of the process. Besides short time plasma instabilities such as arc root fluctuation, process deviations in longer time operations may arise due to electrodes wear and need to be identified by online diagnostics of the plasma spraying process. In this study two online diagnostic systems were employed to investigate the long-term behaviour of a F4-type plasma gun with a convergent and divergent Laval-nozzle. A Plasma Instability Analysis (PISA) system was applied to identify electrode wear and plasma fluctuation phenomena within the gun whereas Particle Flux Imaging (PFI) was used to correlate the electrode wear with the plasma jet characteristics at the nozzle exit. The long-term performance of electrodes was investigated under continuous operation and under periodic reignition of the torch. A good correlation between aberrations in the plasma process observed with PFI and changes in the frequency spectra detected by PISA was found.

Many properties (thermal, electrical, mechanical) of thermal sprayed coatings are strongly linked to the real contacts between the “piled-up” splats. The quality of this contact depends on droplet parameters at impact (size, temperature, velocity) and substrate parameters (temperature, topography). Two different techniques have been developed in order to study the plasma sprayed particle behaviour at impact. The first one allows direct observation under direct current (dc) plasma spray conditions, while the latter one, based on the millimetre sized free falling drop, enables the visualization of flattening phenomena, but at larger scale. These two techniques bring complementary approaches and results. The latter show that flattening time and cooling rate of the lamellae (metallic and ceramic) are improved with the stainless steel substrate surface modification at the nanoscale when corresponding to a positive skewness parameter obtained by preheating it over the transition temperature. Experiments of wettability show that the presence of nanopeaks increases the contact angle of the liquid on the substrates and reduces thermal contact resistance at interface. It has also been shown that, when adsorbates and condensates are not eliminated from the surface, even with a positive skewness, the thermal contact resistance is increased.

The velocity of particles prior to the impact on the substrate surface is a very important factor that determines the deposition characteristics and coating quality in cold spray. The DPV-2000 system is an on-line diagnostic system that simultaneously measures the velocity, temperature and diameter of thermally sprayed particles. In the present study, the DPV-2000 system was employed to measure the velocity and diameter of cold sprayed particles. The effects of pre-setting software parameters of the DPV-2000 system on the measured particle velocities were investigated. The experimental results showed that the pre-setting values of the software parameters produced a significant influence on the accuracy of measured results.

Single-wavelength pyrometers are widely used as a noncontact temperature measurement tool in material processing, petrochemical and laser-machining industries. In addition, these intensity-based IR sensors are used extensively as a diagnostic and health monitoring in the development and research of advanced high-temperature military and commercial gas turbine engines. In contrast to thermocouples, optical pyrometers have several advantages. First, they are easy to install and second they do not bring any disturbance to the measured system. However, they suffer from some problems, in particular the variation of the material emissivity and perturbations introduced by extraneous radiations. Yttria stabilised Zirconia (YSZ) thermal barriers are known to be more emissive and opaque in the 8-14 µm spectral band, therefore we can take advantage using this spectral band in temperature measurement. Spectral emissivities of an YSZ sample were measured using two commercial pyrometers. And a method for adapting commercial wide-band pyrometers (generally used for low temperature measurements) for high temperature measurements of thermal barrier coatings was tested.

Though wire flame spraying is a relatively old thermal spray process, modern equipment permits production of high quality coatings featuring outstanding homogeneity, high density and low roughness due to increased particle velocities as a result of increased combustion gas velocity. Typically spray particles are accelerated to velocities exceeding 250 m/s, if the wires are atomized adequately. In order to make a wide spectrum of coating materials available for wire flame spraying use of cored wires needs to be considered. A high speed camera is used to determine the particle velocity depending on process conditions for massive, grooved cored and tube cored AISI 316L wires. Thereby the influence of the wire design without simultaneous influence by the chemical composition is studied. Additionally nickel based carbide reinforced coatings are sprayed and characterized concerning their microstructure and properties in use.

The effect of torch hardware, operating parameters, and powder type on substrate surface heat flux was quantitatively investigated using calorimeters. The Sulzer-Metco 6P oxyacetylene torch with two nozzles and two air caps and the Alamo PG-550 torch were studied using designed experiments to show the effects of total combustible gas flow, oxy-fuel ratio, air flow, and standoff distance on surface heat flux. Air caps which directed cooling air toward the flame produced lower heat flux than air caps providing gun cooling. For the 6P torch, nozzle geometry did not have a significant effect on heat flux. With low air flow rates, both torches exhibited similar heat fluxes. At high air flows, the surface heat flux of the PG-550 was larger than that of the 6P.

The hybrid spray process that combines arc-spray with HVOF/plasma jet, has recently demonstrated its effectiveness in the deposition of functionally gradient coatings. This approach aims at exploiting the attributes of both the arc-spray technique combined with those of the HVOF/APS technique. This paper presents high speed visualization and plume characterization of an Arc/HVOF hybrid spray gun as well as a twin-wire arc spray gun. The physics of atomization in hybrid spray process is examined using high-speed camera. A DPV-2000 particle diagnostics sensor is used to measure particle velocity, temperature, size and distribution. The influence of feed material, arc-spray parameters, and the jet parameters on the particle characteristics is presented. The differences in the in-flight characteristics between the hybrid and the twin wire arc process have been discussed aided by the observed atomization phenomena with the high speed camera.

Parametric drifts and fluctuations occur during plasma spraying. These drifts and fluctuations originate especially from the electrode wear and intrinsic plasma jet instabilities: the plasma net power varies in this case modifying significantly its thermodynamic properties and transport coefficients hence modifying the momentum and heat transfers to the particles. It is possible to control the in-flight particle characteristics by adjusting continuously the operating parameters, in particular the power parameters. Due to the large amplitudes of these drifts and fluctuations, the strategy to adopt will depend on the required corrections to apply to the particle characteristics. Developing a reliable controller requires: (i) implementation of reliable sensors to accurately diagnose in-flight particle characteristics but also some other parameters, such as surface temperature during spraying; (ii) development of a robust command, to insure the stability of the control system. Fuzzy logic permits to define parametric correction rules and the command can be based on these algorithms; (iii) linking of the robust command to a predictive model. Artificial neural networks, among other artificial intelligence protocols such as genetic algorithms, proved to be able to predict in-flight particle and coating characteristics; (iv) validation of the corrections with an extensive database used as a reference. Coupling neural protocols to fuzzy logic should permit the development of such an independent controller which could adjust in real time the operating parameters as a function of the measured in-flight particles to manufacture a coating with identical conditions among its entire thickness. This paper aims at presenting the methodology and the controller and at simulating the spray operation.