Convolution Neural Network with Laser-Induced Breakdown Spectroscopy as a Monitoring Tool for Laser Cleaning Process

In this study, eight different painted stainless steel 304L specimens were laser-cleaned using different process parameters, such as laser power, scan speed, and the number of repetitions. Laser-induced breakdown spectroscopy (LIBS) was adopted as the monitoring tool for laser cleaning. Identification of LIBS spectra with similar chemical compositions is challenging. A convolutional neural network (CNN)-based deep learning method was developed for accurate and rapid analysis of LIBS spectra. By applying the LIBS-coupled CNN method, the classification CNN model accuracy of laser-cleaned specimens was 94.55%. Moreover, the LIBS spectrum analysis time was 0.09 s. The results verified the possibility of using the LIBS-coupled CNN method as an in-line tool for the laser cleaning process

The Potential of Wind for Energy Production and Water Pumping in Saravan County

Sustainable sources of energy are vital for energy production in remote areas which have difficult access to electricity and grid. Thus, in this paper an initial evaluation of wind resource for over 18 months was done to evaluate the potential of wind energy as a power generation source in a remote village in Saravan county, southeastern Iran. The Weibull distribution is employed to model the wind data at three heights: 10, 30 and 40 meters. The Weibull distribution presented in this study indicates a good compatibility with the measured wind data. Different wind speed parameters such as monthly and diurnal wind speed profiles at different heights, wind direction, turbulence intensity, and etc. have been estimated and analyzed. The results showed the studied site has not the sufficient wind speed and power for development of commercial wind power plants. But the studied site may be suitable for development of small and residential wind turbines. Therefore in the next part of study, energy production of different small wind turbines has been estimated. It was concluded that one of the small wind turbines which has the highest net energy production of 33,685 kWh/ yr and highest capacity factor of 25.6% can be suitable for non-grid connected electrical and mechanical applications, such as local consumption, battery charging, and water pumping. In the last phase of study, the water pumping potential of the studied area has been investigated

Effect of Laser Heat-Treatment and Laser Nitriding on the Microstructural Evolutions and Wear Behaviors of AISI P21 Mold Steel

Laser heat-treatment and laser nitriding were conducted on an AISI P21 mold steel using a high-power diode laser with laser energy densities of 90 and 1125 J/mm2, respectively. No change in surface hardness was observed after laser heat-treatment. In contrast, a relatively larger surface hardness was measured after laser nitriding (i.e., 536 HV) compared with that of the base metal (i.e., 409 HV). The TEM and electron energy loss spectroscopy (EELS) analyses revealed that laser nitriding induced to develop AlN precipitates up to a depth of 15 μm from the surface, resulting in surface hardening. The laser-nitrided P21 exhibited a superior wear resistance compared with that of the base metal and laser heat-treated P21 in the pin-on-disk tribotests. After 100 m of a sliding distance of the pin-on-disk test, the total wear loss of the base metal was measured to be 0.74 mm3, and it decreased to 0.60 mm3 for the laser-nitrided P21. The base metal and laser heat-treated P21 showed similar wear behaviors. The larger wear resistance of the laser-nitrided P21 was attributed to the AlN precipitate-induced surface hardening