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 (Al 2 O 3 -TiO 2 ) 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.

The behavior modeling of Atmospheric Plasma Spray (APS) process requires a global approach which considers interrelated non-linear relationships between coating characteristics / properties in-service and process parameters (power, feedstock injection, kinematics, etc.). Such an approach would permit to reduce the development costs. To reach this objective, the knowledge of the interactions between process parameters plays a relevant role in the optimization. This work intends to develop a behavior model based on fuzzy logic concepts. Here, the model considers the deposition yield as the result of the process and it establishes relationships with power process parameter (arc current intensity, plasma gas total flow rate, hydrogen content) on the basis of fuzzy rules. The model hence permits to discriminate the role and the effects of each power process parameters. The modeling results are compared to experimental data. The specific case of the deposition of alumina-titania (Al 2 O 3 -TiO 2 , 13% by weight) by Atmospheric Plasma Spraying (APS) is considered.

Parametric drifts and fluctuations occur during plasma spraying. These drifts and fluctuations originate especially from the electrode wear and intrinsic plasma jet instabilities: the plasma net power varies in this case modifying significantly its thermodynamic properties and transport coefficients hence modifying the momentum and heat transfers to the particles. It is possible to control the in-flight particle characteristics by adjusting continuously the operating parameters, in particular the power parameters. Due to the large amplitudes of these drifts and fluctuations, the strategy to adopt will depend on the required corrections to apply to the particle characteristics. Developing a reliable controller requires: (i) implementation of reliable sensors to accurately diagnose in-flight particle characteristics but also some other parameters, such as surface temperature during spraying; (ii) development of a robust command, to insure the stability of the control system. Fuzzy logic permits to define parametric correction rules and the command can be based on these algorithms; (iii) linking of the robust command to a predictive model. Artificial neural networks, among other artificial intelligence protocols such as genetic algorithms, proved to be able to predict in-flight particle and coating characteristics; (iv) validation of the corrections with an extensive database used as a reference. Coupling neural protocols to fuzzy logic should permit the development of such an independent controller which could adjust in real time the operating parameters as a function of the measured in-flight particles to manufacture a coating with identical conditions among its entire thickness. This paper aims at presenting the methodology and the controller and at simulating the spray operation.