Achieving Consistency in Mean Particle States for Atmospheric Plasma Sprayed Functionally Graded Coatings

The functionally graded coatings (FGCs) produced by an atmospheric plasma spray process (APSP) must be of uniform quality with continuously varying composition through the thickness. When used as a thermal barrier on a metallic substrate, the coating composition varies from an almost pure metal near the substrate to a pure ceramic adjacent to the outer surface exposed to a hot environment. However, the complexity of the process and the random introduction of noise variables such as fluctuations in the powder injection rate and the arc voltage make it difficult to control the coating quality and composition. These have been shown to depend upon mean values of powder particles’ temperature, speed, and locations, collectively called mean particles’ states (MPSs), just before they impact the substrate. Here we use a science-based methodology to develop a robust adaptive process control algorithm for achieving consistent MPSs for FGCs. We first identify inputs into the APSP that significantly affect the MPSs, and then formulate a relationship between these two quantities. When the MPSs deviate from their desired values, the robust adaptive control algorithm based on the model reference adaptive controller (MRAC) framework is shown to successfully adjust the input parameters to correct them. The utility of this control algorithm is demonstrated to achieve consistently produces high-quality FGCs using NiCrAlY and zirconia powder particles through numerical experiments.