Abstract
Alumina-13 wt% titania wear resistant coatings were deposited using the Atmospheric Plasma Spray (APS) process under several processing conditions. Coating adhesion was then measured locally on cross sections by the indentation test and results were correlated with process variables. In order to identify the most influential factors on adhesion, artificial intelligence was used. The analysis was based on an Artificial Neural Network (ANN) taking into account training and test procedures to predict the dependences of measured property on experimental conditions. This study pointed out primarily that adhesion was largely sensitive to parameters that modified the in-flight particle characteristics (i.e. velocity and temperature). These effects were quantitatively demonstrated and predicted with an optimized neural network structure.
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