Process Optimisation of Plasma Spraying Using Statistical Modelling

Abstract

Abstract Plasma-sprayed coatings are formed by the impact and accumulation of feedstock particles with random sizes, temperatures and velocities on the surface of a substrate. The simulation results obtained for a single particle are not able to represent the behaviour of the feedstock in the process. A statistical analysis is required to describe this stochastic behaviour and in this research, the Monte-Carlo method was used to simulate the particles in the plasma jet. Parcels of groups of particles based on the particle size distribution were employed to provide a simplified and mathematically tractable representation of real feedstock powders. The distributions of particle temperature, velocity and trajectory were directly linked to the particle size distribution and the injection conditions occurring in practice. The statistical approach enables the prediction of the mean value and standard deviation of particle temperature, particle velocity and deposition impact position. The influence of particle-injection velocity, injection position and plasma parameters on the quality of the coatings was studied. The research shows how statistical techniques can be used to control and optimise the plasma-spray process.