Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-17T19:56:19.184Z Has data issue: false hasContentIssue false

On the Implementation of Neural Network Concept to Optimize Thermal Spray Deposition Process

Published online by Cambridge University Press:  17 March 2011

Sofiane Guessasma
Affiliation:
LERMPS, Université de Technologie de Belfort-Montbéliard 90 010 Belfort Cedex, France
Ghislain Montavon
Affiliation:
LERMPS, Université de Technologie de Belfort-Montbéliard 90 010 Belfort Cedex, France
Christian Coddet
Affiliation:
LERMPS, Université de Technologie de Belfort-Montbéliard 90 010 Belfort Cedex, France
Get access

Abstract

Numerous processing parameters, up to fifty, characterize the plasma spray deposition process. A better quality control of the resulting deposits induces a better understanding of their effects on coating formation mechanisms. Numerical models can help to provide such an understanding. From a mathematical point of view, d.c. plasma spray deposition process is assimilated to a nonlinear problem in regards to its variables (operating parameters, environment, etc.). This paper develops a global approach based on an implicit describing of the mechanisms implementing Artificial Neural Networks (ANNs). The global concept and the protocols to implement are presented and developed for an example related to d.c. plasma spray process.

Type
Research Article
Copyright
Copyright © Materials Research Society 2002

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Montavon, G., Hansz, B., Coddet, C., Tourenne, F., Kassabji, F., (in French) Les Cahiers de l'Ingénierie 67, 915 (1998).Google Scholar
2. Herman, H., Scientific American, 256 (9), 112118 (1988).Google Scholar
3. Vardelle, A., Moreau, C., Fauchais, P., MRS Bulletin 7 (25), 3237 (2000).Google Scholar
4. McPherson, R., Thin Solid Films 83, 297310, 1981.Google Scholar
5. Bhadeshia, H.K.D.H., ISIJ International 39, 966979 (1999).Google Scholar
6. Meade, J. Jr and Fernadez, A. A., Math. Comput. Modelling 20, 1944 (1994).Google Scholar
7. Isard, M., Blake, A., International Journal of Computer Vision, 29 (1), 528 (1998).Google Scholar
8. Bair, C., Koch, W., Advances in Neural Information Processing Systems, edited by Touretzky, D.S., Lippman, R.P., Moody, J.E. (San Mateo, CA, USA, 1991) 3, pp. 399405.Google Scholar
9. Tenenbaum, J.B., Freeman, W.T., Advances in Neural Information Processing Systems, edited by Mozer, M.C., Jordan, M.I., Petsche, T. (Cambridge Massachusetts: The MIT Press, MA, USA, 1996) 9, pp. 662668.Google Scholar
10. Thodberg, H.H., IEEE Transactions on Neural Networks 7 (1), 56 (1996).Google Scholar
11. Hirst, J.D., King, R.D., Sternberg, M.J.E., Journal of Computer-Aided Molecular Design 8, 405–20 (1994).Google Scholar
12. Fujita, O., Neural Networks 11, 851 (1998).Google Scholar
13. Brightwell, G., Kenyon, C., Paugam-Moisy, H., Research Report 96-37, LIP, ENS Lyon, Lyon, France (1996).Google Scholar