RESUMEN
Quality inspection in the industrial production field is experiencing a strong technological development that benefits from the combination of vision-based techniques with artificial intelligence algorithms. This paper initially addresses the problem of defect identification for circularly symmetric mechanical components, characterized by the presence of periodic elements. In the specific case of knurled washers, we compare the performances of a standard algorithm for the analysis of grey-scale image with a Deep Learning (DL) approach. The standard algorithm is based on the extraction of pseudo-signals derived from the conversion of the grey scale image of concentric annuli. In the DL approach, the component inspection is shifted from the entire sample to specific areas repeated along the object profile where the defect may occur. The standard algorithm provides better results in terms of accuracy and computational time with respect to the DL approach. Nevertheless, DL reaches accuracy higher than 99% when performance is evaluated targeting the identification of damaged teeth. The possibility of extending the methods and the results to other circularly symmetrical components is analyzed and discussed.
RESUMEN
We investigated the carrier-envelope phase (CEP) stability of hollow-fiber compression for high-energy few-cycle pulse generation. Saturation of the output pulse energy is observed at 0.6 mJ for a 260 µm inner-diameter, 1 m long fiber, statically filled with neon. The pressure is adjusted to achieve output spectra supporting sub-4-fs pulses. The maximum output pulse energy can be increased to 0.8 mJ by either differential pumping (DP) or circularly polarized input pulses. We observe the onset of an ionization-induced CEP instability, which saturates beyond input pulse energies of 1.25 mJ. There is no significant difference in the CEP stability with DP compared to static-fill.