The main goal of the project was to investigate the influence of different gas properties, in combination with different spraying parameters, on the wire arc spraying process. For this purpose, investigations with several gas compositions (pure nitrogen as well as nitrogen combined with hydrogen or ethene – compared to compressed air) have been carried out for different spraying materials which are currently being used in today’s industries, such as copper, carbon (St0.8) and stainless steels (316L). The preheating of the process gas as well as the variation of the gas pressure up to 1,400 kPa (14 bar) were also subject to research. The resulting coating properties have been analyzed in terms of oxide content, porosity and hardness as well as deposition efficiency and adhesive tensile strength. Additionally, in order to enhance the process stability, a system to detect the cause of cold shuts has been developed.

This study investigates the effect of different gas compositions on the size, velocity, and temperature of particles in a wire arc gas jet. In the experiments, two wire materials (316L and G3Si1) were sprayed via compressed air, nitrogen, and a nitrogen-hydrogen mixture and high-speed shadow imaging was used to record in-flight particle characteristics. Deposition efficiency was also measured along with the hardness and oxide content of the coatings. The spraying process, equipment, and test methods employed in the study are described and the results are presented and discussed.

This study investigates the cause of process instabilities in large and small scale arc spraying production equipment and in an experimental spray gun. Arc voltage and current, voltage drop at the wire contact sleeves, and wire feed velocity are recorded and spectrum analysis is used to predict the separation of unmelted wire pieces, or cold shots, during spraying with a view to quality assurance. A high-speed camera with pulsed LED illumination is used to observe droplet detachment and spray jet generation. For each case, applied diagnostics are presented and the results are interpreted with respect to the physical causes of instability and how to avoid them.

One approach for controlling the twin wire arc spray (TWAS) process is to use optical properties of the particle beam like length or brightness of the beam as input parameters for a process control. The idea is that changes in the process like eroded contact nozzles or variations of current, voltage and/or atomizing gas pressure can be detected through observation of optical properties of the particle beam. It can be assumed that if these properties deviate significantly from those obtained from a beam recorded for an optimal coating process the spray particle and so the coating properties change significantly. Thus, the goal is to detect these optical deviations and compensate occurring errors by adjusting appropriate process parameters for the wire arc spray unit. One cost effective method for monitoring optical properties of the particle beam is to apply the process diagnostic system PFI (Particle Flux Imaging): PFI fits an ellipse to an image of a particle beam thereby defining easy to analyze characteristic parameters by relating optical beam properties to ellipse parameters. Using artificial neural networks (ANN) mathematical relations between ellipse and process parameters can be defined. Thus in the case of a process disturbance through the use of an ANN-based control new process parameters can be computed to compensate particle beam deviations. In this paper, it will be shown that different process parameters can lead to particle beams with the same PFI parameters.

Traditionally, large moulds for manufacturing of CFRP (carbon fiber reinforced plastics) parts are machined from a solid metal block making this way of manufacturing very energy and time consuming. Using wire arc spraying thin-walled moulds can be produced by spraying onto an original mould and separating the coating. In order to create a reliable and high quality product the manufacturing process needs to be highly reproducible. Thus the spraying process requires monitoring and control, which can be done using artificial neural networks (ANN). In our approach, for monitoring the process the diagnostic system PFI (Particle Flux Imaging) is used to characterize the spray particle stream, which is essentially achieved by fitting an ellipse to an image of the particle stream. Comparing deviations from a reference ellipse recorded for an “optimal” coating process provides data that can subsequently be used for process control. Investigations performed by the method of design of experiments (DOE) show a very strong correlation of the parameters pressure, current, and voltage with certain parts of the PFI data: for example the semi-minor axis of the ellipse depends linearly on voltage and current but quadratic on pressure. These results can further on be used to control the coating process by ANN. This paper discusses the application of this method and its feasibility for industrial use.

The main goal of this work is to improve the coating properties of three-cathode atmospheric plasma sprayed coatings with respect to porosity and residual stresses. This was done by use of numerical simulation coupled with advanced diagnostic methods. A numerical model for the triple injection of alumina feedstock, as well as acceleration and heating of the powder particles in the characteristic threefold symmetrical plasma jet cross section produced by a three-cathode-plasma torch was developed. The modeling results for the standard injector’s position “0” were calculated and experimentally verified by Laser Doppler Anemometry (LDA). Based on the criteria defined for concentrated feedstock transport and homogeneous thermal treatment of powder particles in the plasma jet, the optimal injection position was found. In the next step a previously developed, coupled CFD-FEM-simulation model was used for simulations of the coating build-up, describing flattening, solidification and deformation due to shrinkage for alumina particles on a rough substrate surface.

The goal of this research group is to homogenize properties of three-cathode plasma sprayed coatings on basis of numerical simulations and advanced diagnostics. Results of the first project phase as well as an outlook to future work are presented. A numerical model for investigation of plasma flow in the free jet, produced by three-cathode torch was developed. Modelling results are verified by plasma diagnostics (Computer Tomography). In order to include particle shrinking effects, coating formation simulation is accomplished by a newly developed model, based on Computational Fluid Dynamics coupled with the Finite Element method, whereat diagnostics carried out in the fields of particle diagnostics. During the next phase of the project, the investigation of the plasma free jet and particle injection by advanced diagnostics and simulation respectively is scheduled. In a subsequent stage the transition from conventional particles to suspensions will be considered. Coating formation simulations are scaled up to dimensions of macroscopic tensile tests. By combining these overarching investigations, appropriate process parameters for homogenized coatings will be obtained.

In the area of atmospheric plasma spraying, newly-developed triple-cathode technologies offer the potential to homogenize the coating properties with respect to porosity and residual stresses. Focused on numerical simulation, combined with advanced diagnostics, the goal of this research group is to adjust these properties systematically. A numerical model that couples fluid dynamic, electro-magnetic and thermal phenomena for a three-cathode torch was developed to investigate the plasma and the electric arc behaviour inside the torch. With help of self-developed computer tomography equipment, which is based on emission spectroscopy, combined with the solution of the Saha equation in thermodynamical equilibrium, it is now possible to reconstruct the 3- dimensional temperature distribution close to the torch outlet. This measurement allows us to confirm the torch numerical modelling. Coating formation is simulated by coupled computational fluid dynamics (CFD) and FEM simulation, so that fluid structure interaction is taken into account. This innovative approach has the advantage to predict residual stresses which occur during cooling and moreover the shrinking effects can be considered. By simulation of the individual regions, in combination with experimental results, which also include the particle velocity, diameter and surface temperature, the corresponding process parameters can be obtained for the desired coating properties.