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Controlling 1000 amps using neural networks
conference contribution
posted on 2017-08-17, 13:27 authored by Raymond Stroud, S. Swallow, John McCardleJohn McCardle, K.T. BurgeThe continued effort to improve working conditions and efficiency in fusion welding has increased automation and taken the operator further from the workpiece. This inherently has increased the demand for improved monitoring and control systems to cope with the increase in throughput. The paper describes an application of two network architectures to control submerged arc welding-a high current, low voltage automatic joining process. A logical discriminator, implemented in hardware is used to identify time/amplitude return echoes derived from ultrasonic interrogation of the arc vicinity and a Kohonen feature map is used to classify arc sound.
History
School
- Design
Published in
IJCNN '93: Proceedings of the 1993 International Joint Conference on Neural Networks, Vols 1-3 IJCNN '93: Proceedings of the 1993 International Joint Conference on Neural Networks, Vols 1-3Pages
1857 - 1860Citation
STROUD, R. ...et al., 1993. Controlling 1000 amps using neural networks. IN: Proceedings of the 1993 International Joint Conference on Neural Networks (IJCNN '93), Nagoya, Japan, 25-29 Oct., pp. 1857- 1860.Publisher
© IEEEVersion
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Publisher statement
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
1993Notes
This paper is in closed access.ISBN
0780314212Publisher version
Language
- en