Abstract
After having established an offline process control based on the optical process diagnostic system PFI (Particle Flux Imaging) the process robustness has been increased by experimental identification of the impact of the noise factors „electrode wear“ and „injector wear“ in the APS process. Neural networks were used for implementing a process controller into a GTV APS process control center. In combination with the PFI system a tool was installed in the process center that is able to predict coating quality by analyzing some characteristics of the plasma and the particle plume. The neural network can be trained for all applications and all feedstock materials. An offline controller is instructing the operator through a desktop in order to train the network. The training, that means the monitoring of the process through different parameter setups and its reactions, is generated and executed automatically. Due to the fact that controlling the process parameters cannot influence every aspect of the coating quality, noise factors have to be regarded. For the APS process the electrode wear and the injector wear were identified as the most influencing noise factors. Both were analyzed by means of Design of Experiment (DOE) and long-term monitoring (200 hrs). The samples were characterized by light microscopy and different coating test methods that were chosen with respect to the coatings functions (e.g. wear resistance). The result of this work is a set of parameters that are as robust against both noise factors as possible and that are adapted to certain changes by a neural network process control.
