Artificial neural networks vs. fuzzy logic: Simple tools to predict and control complex processes - Application to plasma spray processes

The plasma-sprayed coating architecture and in-service properties are derived from an amalgamation of intrinsic and extrinsic spray parameters. These parameters are interrelated; following mostly non-linear relationships. For example, adjusting power parameters (to modify particle temperature and velocity upon impact) also implies an adjustment of the feedstock injection parameters in order to optimize geometric and kinematic parameters. Optimization of the operating parameters is a first step. Controlling these is a second step and consists of defining unique combinations of parameter sets and maintaining them as constant during the entire spray process. These unique combinations must be defined with regard to the in-service coating properties. Several groups of operating parameters control the plasma spray process; namely (i) extrinsic parameters that can be adjusted directly (e.g., the arc current intensity) and (ii) intrinsic parameters, such as the particle velocity or its temperature upon impact, that are indirectly adjusted. Artificial intelligence (AI) is a suitable approach to predict operating parameters to attain required coating characteristics. Artificial Neural Networks (ANN) and Fuzzy Logic (FL) were implemented to predict in-flight particles characteristics as a function of lower process parameters. The so-predicted operating parameters resulting from both methods were compared. The spray parameters are also predicted as a function of achieving a specified hardness or a required porosity level. The predicted operating parameters were compared with the predicted in-flight particle characteristics. The specific case of the deposition of alumina-titania (Al2O3-TiO2, 13% by weight) by APS is considered.