In order to establish and describe, on a middle term, systemic relationships between processing spray parameters, deposit characteristics and in-service properties, an intermediate step consisting in the describing of the effects of processing parameters on the inflight particle state. Diagnostic tools are extensively used to predict causal relationships between plasma spray processing parameters and in-flight particle characteristics. In this paper, experimental results obtained for alumina-titania particles processed while implementing several sets of processing parameters are exploited in order to demonstrate the ability of the model to establish those systemic relationships. The paper deals with the introduction of the artificial neural network (ANN) concept to describe the DC plasma spray process. The protocols used are listed, the ANN model is described, and some results are presented. Paper includes a German-language abstract.

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