Neural network computation for the evaluation of process rendering: application to thermally sprayed coatings

Abstract

In this work, neural network computation is attempted to relate alumina and titania phase changes of a coating microstructure with respect to energetic parameters of atmospheric plasma straying (APS) process. Experimental results were analysed using standard fitting routines and neural computation to quantify the effect of arc current, hydrogen ratio and total plasma flow rate. For a large parameter domain, phase changes were 10% for alumina and 8% for titania with a significant control of titania phase.

Keywords

• Artificial neural network
• Statistical analysis
• Process engineering