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Sensors 2008, 8(10), 6496-6506; doi:10.3390/s8106496
Article

Arc-Welding Spectroscopic Monitoring based on Feature Selection and Neural Networks

* ,
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,
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Photonics Engineering Group, University of Cantabria, Avda. de los Castros S/N, 39005 Santander, Spain
* Author to whom correspondence should be addressed.
Received: 9 October 2008 / Revised: 17 October 2008 / Accepted: 21 October 2008 / Published: 21 October 2008
(This article belongs to the Special Issue Neural Networks and Sensors)
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Abstract

A new spectral processing technique designed for application in the on-line detection and classification of arc-welding defects is presented in this paper. A noninvasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed in two consecutive stages. A compression algorithm is first applied to the data, allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in previous works, giving rise to an improvement in the performance of the monitoring system.
Keywords: Arc-welding; fiber sensor; spectral processing; plasma spectroscopy; on-line monitoring Arc-welding; fiber sensor; spectral processing; plasma spectroscopy; on-line monitoring
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Garcia-Allende, P.B.; Mirapeix, J.; Conde, O.M.; Cobo, A.; Lopez- Higuera, J.M. Arc-Welding Spectroscopic Monitoring based on Feature Selection and Neural Networks. Sensors 2008, 8, 6496-6506.

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