Ablation of bulk polycrystalline zinc in air is performed with single and multiple picosecond laser pulses at a wavelength of 1030 nm. The relationships between the characteristics of the ablated craters and the processing parameters are analyzed. Morphological changes of the ablated craters are characterized by means of scanning electron microscopy and confocal laser scanning microscopy. Chemical compositions of both the treated and untreated surfaces are quantified with X-ray photoelectron spectroscopy. A comparative analysis on the determination of the ablation threshold using three methods, based on ablated diameter, depth and volume is presented along with associated incubation coefficients. The single pulse ablation threshold value is found to equal 0.21 J/cm². Using the calculated incubation coefficients, it is found that both the fluence threshold and energy penetration depth show lesser degree of incubation for multiple laser pulses.

Hot-dipped galvanized steel is processed with short- and ultrashort-pulsed lasers in air at near-infrared wavelengths with pulse durations ranging from 350 fs to 241 ns. The morphology of the ablated craters (processed over a range of laser fluence levels and a number of laser pulses) is analyzed by confocal laser scanning microscopy and scanning electron microscopy. The ablation threshold of galvanized steel is found to increase with laser pulse durations following a simple power Fth=A.τB law. Longer pulse durations in the nanosecond regime, as compared to pulse durations in the picosecond and femtosecond regime, result in higher ablation efficiency and energy penetration depth at the cost of surface quality.

Laser ablation of galvanized steel at a wavelength of 343, 515 and 1030 nm was compared for single as well as multiple picosecond laser pulses. The characteristics of ablated craters, such as ablation rate, crater shape and chemical composition, in relation to the processing parameters were studied. Surface morphology of the laser ablated craters were characterized with the help of confocal laser scanning microscopy and scanning electron microscopy. Chemical compositional and crystallographic changes were analyzed by energy-dispersive X-ray spectroscopy and electron backscatter diffraction respectively. Three ablation regimes were identified in the ablation process of galvanized steel. For equal amount of fluence, ablation rates are found to increase with decreasing laser wavelength. Analyzing the crater shape and the cross-sectional chemical composition, three possible applications are identified for three different wavelengths when processing galvanized steel with picosecond pulsed lasers, namely coating removal, surface texturing and micro-drilling.

In the last two decades, unmanned aerial vehicle (UAV) technology has been widely utilized as an aerial survey method. Recently, a unique system of self-deployable and biodegradable microrobots akin to winged achene seeds was introduced to monitor environmental parameters in the air above the soil interface, which requires geo-localization. This research focuses on detecting these artificial seed-like objects from UAV RGB images in real-time scenarios, employing the object detection algorithm YOLO (You Only Look Once). Three environmental parameters, namely, daylight condition, background type, and flying altitude, were investigated to encompass varying data acquisition situations and their influence on detection accuracy. Artificial seeds were detected using four variants of the YOLO version 5 (YOLOv5) algorithm, which were compared in terms of accuracy and speed. The most accurate model variant was used in combination with slice-aided hyper inference (SAHI) on full resolution images to evaluate the model’s performance. It was found that the YOLOv5n variant had the highest accuracy and fastest inference speed. After model training, the best conditions for detecting artificial seed-like objects were found at a flight altitude of 4 m, on an overcast day, and against a concrete background, obtaining accuracies of 0.91, 0.90, and 0.99, respectively. YOLOv5n outperformed the other models by achieving a mAP0.5 score of 84.6% on the validation set and 83.2% on the test set. This study can be used as a baseline for detecting seed-like objects under the tested conditions in future studies.