Artificial neural networks implementation in plasma spray process: Prediction of power parameters and in-flight particle characteristics vs. desired coating structural attributes

Artificial neural networks (ANN) were implemented to predict atmospheric plasma spraying (APS) process parameters to manufacture a coating with the desired structural characteristics. The specific case of predicting power parameters to manufacture grey alumina (Al(2)O(3)-TiO(2), 13% by wt.) coatings was considered. Deposition yield and porosity were the coating structural characteristics After having defined, trained and tested ANN, power parameters (arc current intensity, total plasma gas flow, hydrogen content) and resulting in-flight particle characteristics (average temperature and velocity) were computed considering several scenarios. The first one deals at the same time with the two structural The others one deals with one structural characteristic as constraint while the characteristics as constraints. The other is fixed at a constant value. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.