Spectral processing technique based on feature selection and artificial neural networks for arc-welding quality monitoring

The validity of a processing technique, based on feature selection and artificial neural networks, when applied to arc-welding on-line quality monitoring is analyzed. An optical fiber embedded within the welding torch captures the plasma radiation, which is delivered to a CCD spectrometer. In the proposed solution the spectral analysis is performed in two stages: dimensionality reduction is accomplished using a feature selection algorithm and, after that, an artificial neural network carries out the spectral identification task. The validity of the technique has been successfully demonstrated by means of several experimental welding tests.