# Development of High-Power Ultrasonic System Dedicated to Metal Powder Atomization
## 4 Results and Discussion
### Observation of Ultrasonic Atomization of Water by High-Speed Camera
The areas representing the surface of the sonotrode and the liquid covering it, which has not yet been completely atomized, were removed (Figure 12d). The next step was to adjust the background (Figure 13). For this purpose, a median filter was used to remove random peaks and a low-pass (averaging) filter was used to extract the background. This is achieved by using a filter mask the size of which (in pixels) exceeds the size of the largest droplet, which leads to the removal of droplets from the image and averaging the background. The background thus extracted was subtracted from the original image and the image was ready for the threshold process. The threshold value was chosen so that the image after binarization only took into account droplets without the background. Figure 11: Droplet distribution for 20 kHz (**a**,**c**) and 70 kHz (**b**,**d**). After image binarization, the \"regionprops\" function of the Matlab image pressing package was used. This function returns the results of measurements of objects in a black and white binary image. It allows determining the x and y coordinates of the detected regions and their equivalent diameter. This function finds unique objects in a binary  
Figure 12: Selection of images for the analysis (**a**) 0 \(\upmu\)s, (**b**) 80 \(\upmu\)s, (**c**) 120 \(\upmu\)s, (**d**) extracted fragment. Figure 13: Image background extraction and alignment. image using 8-connected 'neighboring' regions of pixels (oriented horizontally, vertically or diagonally) for 2-D images. So, the pixels are considered as a part of the same object if their edges or corners are connected. The exemplary results for 70 kHz ultrasonic atomization set-up are shown in Figure 14.  
In the next step, the droplet size distribution results were obtained and compared. A histogram showing the comparison of results obtained for 20 kHz and 70 kHz transducers is presented in Figure 15.  
Figure 14: Droplets detection: (**a**) gray scale image, (**b**) image after binarization, (**c**) image with recognized regions (marked with the red circles) and (**d**) verification of the image processing. Figure 15: Comparison of droplet size distribution for 20 kHz and 70 kHz. It can be clearly seen that for a 20 kHz transducer, particles of 35 \(\upmu\)m dominate, and at least 50% of the produced particles are in the range of 35-55 \(\upmu\)m. It should be noted that this result corresponds to the average droplet size calculated analytically (2), i.e., 55 \(\upmu\)m. In the case of a 70 kHz transducer, a clear decrease in particle size can be seen. The size of 10 \(\upmu\)m dominates, and at least 50% of the particles are in the range of 10-25 \(\upmu\)m. The analytically calculated size is 24 \(\upmu\)m for 70 kHz transducer frequency. Moreover, the results obtained experimentally for both 20 kHz and 70 kHz systems correspond well to the results that can be found in the literature, as calculated in a range of 20 to 100 kHz by [PERSON] and [PERSON] [22], numerically analyzed in a range of 50 to 200 kHz by [PERSON] et al. [23] or experimentally tested for 35 kHz ultrasonic system by [PERSON] [24]. In turn, the analytically calculated values for liquid metal are 86 \(\upmu\)m for 20 kHz and 37 \(\upmu\)m for 70 kHz, respectively. The obtained results allow concluding that for liquid metals, the size of the obtained particles will be about 30-40% higher than for water, for both considered oscillating systems; that would be a very positive desired outcome. The current interest in metal powders and metal alloys, due to the characteristics of modern manufacturing technologies, mainly concerns materials in the range of 10-100 \(\upmu\)m [25; 26].