In Atmospheric plasma spray process, the in-flight particle characteristics such as their particle size, velocity and surface temperature influence significantly their flight duration and consequently their melting degree. The knowledge of the correlations between process parameters and in-flight particle characteristics is very important for optimizing the coating qualities. Artificial neural networks was trained and optimized to establish the relationships linking in-flight particle average diameter and process parameters to in-flight particle average velocity and surface temperature. Then, the established ANN relationships permitted to determine the in-flight particle average velocity and surface temperature versus their diameter for given process parameters. These predicted average velocity and surface temperature data were then used to determine the time for complete melting of the particle and its dwell-time before impact by an analytical model for given operating conditions. Crown Copyright © 2009.
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