Paper
9 April 2007 Arc-welding defect detection by means of principal component analysis and artificial neural networks
Author Affiliations +
Abstract
The introduction of arc and laser welding in the aerospace, automotive and nuclear sectors, among others, has led to a great effort in research concerning quality assurance of these processes. Hence, an on-line, real-time welding monitor system able to detect instabilities affecting the welding quality would be of great interest, as it would allow to reduce the use of off-line inspection techniques, some of them by means of destructive-testing evaluation, improving process productivity. Among several different approaches, plasma optical spectroscopy has proved to be a feasible solution for the on-line detection of weld defects. However, the direct interpretation of the results offered by this technique can be difficult. Therefore, Artificial Neural Networks (ANN), due to their ability to handle non-linearity, is a highly suitable solution to identify and detect disturbances along the seam. In this paper plasma spectra captured during welding tests are compressed by means of Principal Component Analysis (PCA) and, then, processed in a back propagation ANN. Experimental tests performed on stainless steel plates show the feasibility of the proposed solution to be implemented as an on-line arc-welding quality monitor system.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
P. B. García-Allende, J. Mirapeix, A. Cobo, O. M. Conde, and J M. López-Higuera "Arc-welding defect detection by means of principal component analysis and artificial neural networks", Proc. SPIE 6541, Thermosense XXIX, 654113 (9 April 2007); https://doi.org/10.1117/12.718392
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Cited by 2 scholarly publications.
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KEYWORDS
Principal component analysis

Plasma

Artificial neural networks

Defect detection

Spectroscopy

Plasma spectroscopy

Charge-coupled devices

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