Designing expert system using neural computation in view of the control of plasma spray processes

This paper aims at integrating the artificial intelligence methodologies in a quality control of ceramic coating fabrication using the atmospheric plasma spray (APS) process. In such a way, the average velocity, temperature and diameter of thermally sprayed Al2O3-13 wt.% TiO2 particles before impinging the work piece and forming a deposit are monitored. Then, as these particle characteristics represent the most pertinent indicators of the coating properties and characteristics reproducibility, they are chosen as the output of an expert system based on neural computation. The model is built also considering at the system input the plasma and particle powder injection-processing parameters. After an optimisation procedure, the predicted results are compared to the results of experimental data resulting from a non-intrusive sensor conventionally used by industrials to control the coating quality. The good agreement found between these results permits to establish the overall effect of each processing parameter on the in-flight particle characteristics.