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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. Burge
The 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 1-3

Pages

1857 - 1860

Citation

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

© IEEE

Version

  • VoR (Version of Record)

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

1993

Notes

This paper is in closed access.

ISBN

0780314212

Language

  • en

Location

Nagoya, Japan

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