Abstract

Functionally graded coatings (FGCs) have a material composition continuously varying through the thickness but uniform in the surface parallel to the coated substrate. 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. Challenging issues in producing high quality FGCs in the presence of external disturbances with an atmospheric plasma spray process (APSP) include controlling the mean temperature, the mean axial velocity, and the positions of the constituent material particles when they arrive at the substrate to be coated. The unavoidable disturbances include fluctuations in the arc voltage and clogging of the powder in the delivery system. For a two-constituent coating, this work proposes using three modified robust model reference adaptive controllers based on the σ-modified laws and low frequency learning. One controller adjusts the current and flow rates of argon and hydrogen into the torch. The other two controllers adjust the distance of the two powder injector ports from the plasma jet axis and the average injection velocity of each powder. It is shown through numerical experiments that the three controllers implemented in an APSP consistently produce high-quality FGCs.

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