## 4 Experimental Section
### Testing and Results  
Using a Piezoelectric disc as a hydrophone and obtaining a frequency domain signal will tell us about the amplitude of the frequencies in the agitated liquid medium. As discussed in the previous section; i.e., Section 4.1., different diameter piezoelectric discs were dipped inside of the liquid medium, and their related response was obtained with the help of an oscilloscope. The oscilloscope time domain signal was converted into a frequency domain signal using MATLAB, and the behaviour of the waves induced inside of the system was observed. An FFT response of the system is shown in Figure 10; the graphs show that other frequencies are negligible as compared with the driving frequency inside the tank. Figure 8: Aluminuim Foil Corrosion Test. Figure 9: Image Acquisition Setup. To efficiently drive the ultrasonic cleaner, a pulsed driving circuit is required, as explained by [31]. To achieve pulse driving, a small DC link capacitor of value (1 uF/400 V) was used, and the FFT result obtained inside of the tank is shown in Figure 11.  
Artificial intelligence has been widely used to find the ultrasonic cleaner's efficiency. [PERSON] et al. [32], proposed a back propagation artificial neural network (BPANN) to find the material removal from an object that has been cleaned using ultrasonic cleaning. For the second series of experiments, a similar approach was used to find the efficiency of ultrasonic cleaning. For this purpose, a total of 100 images of aluminum foils were taken. These 100 images were then augmented with gaussian noise, contrast, and brightness. After the augmentations, the total number of images in the dataset reached 300. These images were then labelled using labelling software with two different class labels; for premature perforation \"low\" label was assigned, and a \"high\" label was assigned to mature perforations. The dataset was then trained on YOLOv5 using Google Collaborate, which is an online cloud computing system which provides access to free GPUs. The overall results obtained using YOLOv5 models were not satisfactory. The best results were obtained using (YOLO v5s) weights, which are shown in Table 3.  
Figure 11: Frequency Response of Ultrasonic Cleaner using Proposed Circuit. Figure 10: The frequency response of the Ultrasonic Cleaner, obtained at a single operating frequency (42 kHz) using Hydrophone. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sr no.** & **Class** & **Labels** & **Precision** & **Recall** & **mAP 0.5** & **mAP 0.5:0.95** \\ \hline
1 & All & 272 & 0.445 & 0.562 & 0.527 & 0.231 \\
2 & High & 156 & 0.565 & 0.891 & 0.869 & 0.415 \\
3 & Low & 116 & 0.326 & 0.233 & 0.185 & 0.0475 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Precision and Recall of different Labels. Due to less accurate results, the above technique was terminated and another approach was adopted. For this approach python, open cv, was used for image processing. The dataset was the same as what was used for the YOLO V5 work. A GUI was also introduced for this purpose as shown in Figure 12. The user must upload the corroded foil image to the GUI and input the size of the foil in mm. The designed software then calculated the pixels of the image. With the size of an image, as provided by the user and the image pixel read by the software from the file, the software then calculates the total image area and pixel per square mm. A GUI is shown in Figure 13 along with all of the results of the image. The image was then converted into grayscale as shown in Figure 14, and thresholding was applied; i.e., the pixels having values less than 127 were termed as perforated pixels and others were termed as non-perforated pixels. In this way, the software calculated the perforated and non-perforated pixels. Then. by having the ratio of the perforated and non-perforated pixels available, the percentage of complete perforation in the foil was obtained. Figure 12: Aluminium Foil of Dimensions (120 mm \(\times\) 120 mm). Figure 13: GUI for proposed system and Results.