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

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 t...

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Bibliographic Details
Published in:International journal for simulation and multidisciplinary design optimization 2017, Vol.8 (A10), p.A10-8
Main Authors: Guessasma, Sofiane, Bassir, David
Format: Article
Language:English
Subjects:
Aluminum oxide
Artificial neural network
Computation
Engineering Sciences
Flow velocity
Humanities and Social Sciences
Methods and statistics
Neural networks
Parameters
Phase transitions
Process engineering
Sprayed coatings
Statistical analysis
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Summary: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.
ISSN:1779-627X
1779-6288
1779-6288
DOI:10.1051/smdo/2017003