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The real time analysis of acoustic weld emissions using neural networks

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conference contribution
posted on 31.08.2017 by K.L. Burge, T.J. Harris, Raymond Stroud, John McCardle
Artificial Neural Networks (ANNs) are becoming an increasingly viable computing tool in control scenarios where human expertise is so often required. The development of software emulations and dedicated VLSI devices is proving successful in real world applications where complex signal analysis, pattern recognition and discrimination are important factors. An established observation is that a skilled welder is able to monitor a manual arc welding process by subconsciously changing the position of the electrode in response to an adverse change in audible process noise. Expert systems applied to the analysis of chaotic acoustic emissions have failed to establish any salient information due to the inabilities of conventional architectures in processing vast quantities of erratic data at real time speeds. This paper describes the application of a hybrid ANN system, utilising a combination of multiple ANN architectures and conventional techniques, to establish system parameter acoustic signatures for subsequent on line control.

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Published in

Proceedings of the 6th International Conference on the Joining of Materials Proceedings of the 6th International Conference on the Joining of Materials

Pages

60 - 67

Citation

BURGE, K.L. ...et al., 1993. The real time analysis of acoustic weld emissions using neural networks. IN: Al-Erhayem, O.E. (ed.) Proceedings of the 6th International Conference on the Joining of Materials, Helsingor, Denmark, April 5-7th., pp. 60-67.

Publisher

European Institute for the Joining of Materials

Version

AM (Accepted Manuscript)

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 is a conference paper.

ISBN

8789582020

ISSN

2246-0160

Language

en

Location

Helsingor, Denmark

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