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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/26101

Title: The classification of metal transfer mode using neural networks
Authors: Vincent, Daniel
McCardle, John
Stroud, Raymond
Issue Date: 1995
Publisher: © IEEE
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.
Abstract: To 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.
Description: This paper is in closed access.
Version: Published
DOI: 10.1109/ICNN.1995.488232
URI: https://dspace.lboro.ac.uk/2134/26101
Publisher Link: https://doi.org/10.1109/ICNN.1995.488232
ISBN: 0780327691
Appears in Collections:Closed Access (Design School)

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