Using Artificial Neural Network to predict the particle characteristics of an Atmospheric Plasma Spray process

Thermal Spray is a general term for a group of coating processes used for metallic or non-metallic coatings to protect a functional surface or improve its performance. There are several processing parameters defining the coating quality and they must be combined and planned in an optimised way in order to have the selected coating exhibit the desired properties. To have the proper combination is critical as it influences both the cost and coating characteristics. The plasma spray process combines the highest number of processing parameters and to have full control over the system, one of the major challenges is to understand the parameters interdependencies, correlations and their individual effects on coating properties and characteristics. A robust methodology is thus required to study these interrelated effects. This paper proposes a new approach based on Artificial Neural Network (ANN) to play this role. The obtained database of the input processing parameters and the output particle characteristics is used to train, validate and optimise the neural network. The optimisation steps are discussed and the predicted outputs are compared with the experimental ones.