Controlling the Twin Wire Arc Spray Process Using Artificial Neural Networks (ANN)

Abstract

Abstract 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.