In thermal spray process, the in-flight particle characteristics such as particle size, velocity and temperature influence significantly their flight duration as well as their melting degree. Consequently, they influence the splat formation and ultimately the coating properties. Thus, the knowledge of the interactions between the process parameters and the in-flight particle characteristics is very important for optimizing the coating properties. Artificial Neural Network (ANN) concept was used to predict in-flight particle velocity and temperature considering the case of alumina (Al2O3-TiO2) coatings. Databases of in-flight particle characteristics (diameter, velocity and temperature) versus spray process parameters (arc current intensity, hydrogen rate and plasma gas composition) were collected. ANN was trained with the database to establish the relationships linking the particle diameter and spray process parameters to particle velocity and temperature. Then, the established ANN relationships permitted to determine the inflight particle velocity and temperature versus their diameter for given spray process parameters. These velocity and temperature data were then used to determine the time for complete particle melting and the particle dwell-time before impact by an analytical model for given operating conditions.

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