# A Through-Transmission Ultrasonic Method for the Detection of Ferrite Tile Defects
## 4 Design and Experimentation of the Ultrasonic Transmission System
### Experimental Results  
#### 4.3.1 Transmission Test of Aluminum Alloy Sample  
The purpose of the ultrasonic transmissive experiments is to validate the detection sensitivity and capability of the proposed ultrasonic transmissive method for external defect detection. Precise cavities of varying sizes and depths were fabricated to simulate internal defects, and the defect detection system's ability to detect defects of different sizes  
Figure 6: Fixture set assembly: (**a**) Assembly; (**b**) base; (**c**) probe clamp; (**d**) sample clamp. was tested. The ferrite tiles have a sound impedance of 28.3. Among commonly used CNC machining materials, as shown in Table 1, the aluminum alloy exhibits similar acoustic properties, with sound impedance and velocity relatively close to those of ferrite tiles. Therefore, the aluminum alloy was used to quantitatively validate the defect detection pattern using the ultrasonic transmissive method. The first set of experiments focused on artificially created aluminum alloy samples with defects. Circular hole defects of different depths (7 mm, 14 mm, 21 mm, 28 mm, and 35 mm) were CNC-machined at five equidistant points along the depth. The distribution of defects by depth and an example can be seen in Figure 8 below. The defect sizes were 0.4 mm, 0.6 mm, 0.8 mm, 1.0 mm, and 1.2 mm. Transmissive experiments were conducted on aluminum alloy samples with different defect sizes at the same depth to test the detection capability under various depth conditions and verify the simulation conclusions. Additionally, based on the machining conditions, a set of extremely small defect samples with a minimum defect diameter of 0.1 mm was prepared. These samples contain near-surface defects, with defect depths of 3 mm or shallower, resulting in the overlap of ultrasonic pulse echo signals with surface echoes, creating a near-surface detection blind zone. These samples were used to evaluate the ultrasonic transmissive method's capability to detect near-surface defects. By performing ultrasonic A-scans (Amplitude Scan) on the defect-containing regions of the samples, the transmission energy of ultrasonic waves was measured. Figure 9 shows the experimental results of signal energy variation with defect radius for each depth, including near surface depth. As shown in the figure, the regularity is observed that as the defect size increases, the transmission signal energy decreases at all depths. The correlation coefficients between the defect diameter and transmission signal energy are both above 0.97, indicating a significant correlation between the two parameters. The attenuation functions of transmission signal energy at all depths have a correlation greater than 0.975 with the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Acoustic Impedance gm/(cm\({}^{2}\)-s)** & **Sound Velocity (25 \({}^{\circ}\)C) km/s** \\ \hline \hline Ferrite & 28.3 & 6.7 \\ \hline Aluminum & 17.0 & 6.3 \\ \hline Copper & 41.6 & 4.7 \\ \hline Steel and Stainless & 45.4 & 5.8 \\ \hline Iron & 45.4 & 5.9 \\ \hline Magnesium & 10.1 & 5.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Acoustic indicators of common materials. Figure 7: Example of ultrasonic transmission signal. simulation results. The maximum residual difference between the measured normalized transmission energy and the predicted value from the simulated fitted curve is 0.12, and this maximum is from samples with a radius of 0.5 mm in the depth = 21 mm group. This discrepancy might be attributed to the metal block's rust formation due to prolonged immersion in water and the potential errors in the CNC machining of the defects. Figure 8: Examples of defects in aluminum alloys: **(a)** Aluminum alloy; **(b)** alloy immersed in coupler. Figure 9: Transmission Experiments of Aluminum Alloys with Different Depth Defects. The experimental results demonstrate a significant difference in transmission signal energy between defect-free and defective samples, allowing for defect detection and identification of defect sizes.