Laser cleaning is a good alternative to ablate and remove contaminants from different samples. To meet the practical demand, we present the elemental analysis of Q235 steel samples, using laser-induced breakdown spectroscopy (LIBS) to enhance the laser cleaning process. Two samples were selected and kept in water and soil for 4 and 7 days, respectively. Half of the samples were then cleaned using the laser cleaning method. The objectives were to promote the application of laser cleaning, generalize the LIBS for the laser cleaning settings, and identify the different sources of contaminations. Numerous elements were determined by analyzing the LIBS spectra, including Fe, Mn, Cu, Si, Ni, Cr, C, S, and P. After 20 excitation cycles, LIBS signals were comparatively stable and could participate in the ensuing classification modeling procedure. The contaminated samples were noticeably stronger overall than the uncontaminated samples, with the higher the concentration of a certain element, the higher the characteristic spectral intensity of LIBS. The typical spectral intensity and concentration of the two samples were found to be in good agreement.
This paper proposed a dynamic resource allocation scheme in nonlinear elastic optical networks based on deep reinforcement learning, which achieves significant blocking probability reductions of more than 44.1% compared with baseline algorithms.
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