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The classification of metal transfer mode using neural networks
conference contribution
posted on 2017-08-17, 12:32 authored by Daniel Vincent, John McCardleJohn McCardle, Raymond StroudTo develop a control strategy for a Metal Inert Gas (M.I.G.) welding system it is necessary to classify several parameters in order to describe the process state. Neural networks have been identified as an appropriate processing technology because of the noisiness of weld data and the non-linearity of the relationships between many of the process parameters. This paper describes the application of neural networks to the classijication of metal transfer mode. We report on the analysis of the data, network selection, network development and evaluation of the final system.
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IEEE International Conference on Neural Networks Proceedings, Vols 1-6 IEEE International Conference on Neural Networks Proceedings, Vols 1-6Pages
522 - ?Citation
VINCENT, D., MCCARDLE, J. and STROUD, R., 1995. The classification of metal transfer mode using neural networks. Presented at the IEEE International Conference on Neural Networks Proceedings, Perth, Western Australia, 27 Nov.-1 Dec.Publisher
© IEEEVersion
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This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
1995Notes
This paper is in closed access.ISBN
0780327691Publisher version
Language
- en