# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 4 Discussion and Analysis of Results
### Experimental Results and Analysis  
Consistent with the damage locations set in the simulation model, PZT unit No. 5 in the middle was used to generate the Lamb wave, while the other eight PZTs received the Lamb wave signals and collected experimental data under both single-damage and double-damage conditions. The numbering and arrangement in these figures are consistent with the simulation results, and the quantitative comparison of API and SNR values is provided in Table 2.  
The experimental single-damage imaging results are illustrated in Figure 11. Consistent with the simulation findings, all three methods achieved accurate damage localization within the marked boundaries (Figure 11a,c,e). The damage regions reconstructed by MVDR and CNN (Figure 11d,f) exhibited sharper intensity profiles compared to the DAS (Figure 11b). However, due to experimental noise in the acquired signals, the imaging noise levels increased across all methods. Notably, even the MVDR results (Figure 11d) showed slightly elevated noise compared to their simulated counterparts (Figure 6d). A quantitative analysis confirms that the CNN method effectively suppresses most background noise relative to the DAS method, yielding a more compact damage representation. This was further validated by the transverse intensity profiles at y = 150 mm (Figure 12), where the CNN demonstrated a sharper amplitude distribution than the DAS. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**DAMAGE**} & \multicolumn{3}{c}{**API**} & \multicolumn{3}{c}{**SNR (dB)**} \\ \cline{2-7}  & **DAS** & **MVDR** & **CNN** & **DAS** & **MVDR** & **CNN** \\ \hline \((0,150)\) & 25.82 & 4.13 & 8.18 & 16.44 & 82.32 & 54.81 \\ \((-20,180)\) & 28.16 & 3.96 & 7.72 & 15.97 & 73.08 & 57.16 \\ \((20,130)\) & 26.36 & 4.08 & 7.69 & 15.85 & 70.29 & 55.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative analysis and comparison of evaluation metrics for experimental imaging results. Figure 10: Transverse damage index profiles at y = 180 mm for different imaging methods based on simulation data: (**a**) full view, (**b**) zoom-in view. Figure 9: Transverse damage index profiles at y = 130 mm for different imaging methods based on simulation data: (**a**) full view, (**b**) zoom-in view. For dual-damage imaging (Figure 13), all three methods accurately identified and localized both damages (Figure 13,c,e). The sharpness of MVDR and CNN results (Figure 13,f) remained superior to those of the DAS (Figure 13), though noise in the CNN output (Figure 13) slightly exceeded that of the MVDR (Figure 13). These observations were corroborated by the transverse intensity profiles at the two damage locations  
Figure 11: The imaging results of three imaging methods on single-damage experimental data: (a) DAS, 2D, (b) DAS, 3D, (c) MVDR, 2D, (d) MVDR, 3D, (e) CNN, 2D, (f) CNN, 3D. Figure 12: Transverse damage index profiles at \(y=150\) mm for different imaging methods based on experimental data: (a) full view, (b) zoom-in view. Figure 16 presents the three-dimensional imaging results obtained using the FCNN method [24], based on experimental single- and dual-damage datasets. The results demonstrated that the FCNN method achieved accurate damage identification and localization. However, a comparative analysis between Figure 16a,b, Figures 11f and 13f revealed that the main lobes of the damage regions reconstructed by the FCNN method were significantly broader than those generated by the proposed CNN method. This indicates that the FCNN-based imaging yielded larger damage areas, thereby reducing imaging resolution. In contrast, the CNN method produced a more compact damage representation, which enhanced spatial resolution. Furthermore, while both methods suppressed background noise to some extent, the CNN method exhibited noise in multi-damage scenarios (Figure 13f), suggesting that further improvements in noise suppression are required to fully mitigate the interference between damage signals.