Adaptive Process Control for Achieving Consistent Mean Particles' States in Atmospheric Plasma Spray Process

The coatings produced by an atmospheric plasma spray process (APSP) must be of uniform quality. 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 that has been shown to depend upon mean values of powder particles' temperature and speed, collectively called mean particles' states (MPSs), just before they impact the substrate. Here we use a science-based methodology to develop an adaptive controller for achieving consistent MPSs. 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 adaptive controller based on the model reference adaptive controller (MRAC) framework is shown to successfully adjust the input parameters to correct them. The performance of the controller is tested via numerical experiments using the software, LAVA-P, that has been shown to well simulate the APSP. The developed adaptive process controller is further refined by using sigma (σ) adaptive laws and including a low-pass filter that remove high-frequency oscillations in the output. The utility of the MRAC controller to achieve desired locations of NiCrAlY and zirconia powder particles for generating a 5-layered coating is demonstrated. In this case a pure NiCrAlY layer bonds to the substrate and a pure zirconia makes the coating top. The composition of the intermediate 3 layers is combination of the two powders of different mass fractions. By increasing the number of intermediate layers, one can achieve a continuous through-the-thickness variation of the coating composition and fabricate a functionally graded coating.