Artificial Neural Network application for predicting in-flight particle characteristics of an atmospheric plasma spray process

Thermal spray consists of a group of coating processes that are used to apply metal or non-metallic coatings to protect a functional surface or to improve its performance. There are some 40 processing parameters that define the overall coating quality and these must be selected in an optimized fashion to manufacture a coating that exhibits desirable properties. The proper combination of processing variables is critical since these influence the cost as well as the coating characteristics.Because of this high number of processing parameters, a major challenge is to have full control over the system and to understand parameter interdependencies, correlations and their individual effects on the in-flight particle characteristics, which have significant influence on the in service coating properties. This paper proposes an approach, based on the Artificial Neural Network (ANN) method, to play this role and illustrates the model's design, network optimization procedures, the database handling and expansion steps, and analysis of the predicted values, with respect to the experimental ones, in order to evaluate the network's performance.

Research highlights
► Artificial Neural Network (ANN) successfully predicted particle characteristics.
► Predicted values in overall show minimum scatter with the experimental values.
► Expansion of database improved trained ANN's performance.
► Results show possibility of ANN to set up a control system for the spray process.