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.
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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.
Garcia-Allende PB, Mirapeix J, Conde OM, Cobo A, Lopez- Higuera JM. Arc-Welding Spectroscopic Monitoring based on Feature Selection and Neural Networks. Sensors. 2008; 8(10):6496-6506.
Garcia-Allende, P. B.; Mirapeix, Jesus; Conde, Olga M.; Cobo, Adolfo; Lopez- Higuera, Jose M. 2008. "Arc-Welding Spectroscopic Monitoring based on Feature Selection and Neural Networks." Sensors 8, no. 10: 6496-6506.