Control of Wire Arc Spraying Using Artificial Neural Networks for the Production of Thin-Walled Moulds for Carbon Fiber Reinforced Plastics

摘要

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.