Plasma spray process modelling using artificial neural networks: Application to Al₂O₃-TiO₂ (13% by weight) ceramic coating structure

Abstract
Thermal spraying is a versatile technique of coating manufacturing implementing large variety of materials and processes. The manufacture control is constrained by the understanding of the physical phenomena occurring during the spraying. It is however penalized by the large number of processing parameters (up to 50), their interdependencies, their correlations with the coating attributes and the stability of the process. Numerous statistical, heuristic or physical models intended to response to these constrains, very often partially because considering some aspects of the process. This work aims at considering a more global approach based on a powerful statistical methodology using artificial intelligence. Following this approach, the physical phenomena are encoded in a structure called Artificial Neural Network (ANN). An application of the ANN methodology is discussed in the case of the APS spray process. Some processing parameters categories are related to some coating properties for alumina-titania (13% by weight) ceramic coatings. ANN optimization is presented and discussed. Predicted results show globally a well agreement with the experimental values. Some conclusions point out the advantages of the ANN on the conventional methods, such as the design of experiments, used usually to recognize the controlling factors in a process.