## 4 Result
### Comparison Between LVT and PT for BVID Detection in CFRP  
To better understand the capability of LVT in detecting BVID in CFRP composites, its results were compared with those from PT, a well-established thermal NDT method. Both techniques were applied to the same set of impacted CFRP samples. The thermal data from each technique were processed using a consistent approach that included both PCA and RPCA. The best principal component (PC) selected based on the visual contrast of the defect was then used to calculate the SNR, providing a quantitative basis for comparison.  
In this context, PT serves as a benchmark. PT uses a strong, short-duration heat pulse that typically results in high thermal contrast [51]. LVT, in contrast, relies on mechanical excitation to generate heat at internal defect sites through vibration or friction. These distinct excitation mechanisms result in different thermal responses, which in turn affect image contrast and the effectiveness of PCA-based defect isolation.  
For consistency, only upper-positioned samples of LVT were included in the analysis. Earlier findings indicated that defect visibility in LVT was mostly better when the specimen was positioned as the upper sample in the dual-sample setup.  
For the calculation of PT data, a region of interest (ROI) measuring 130 x 130 pixels was defined. Rectangular patches were selected within the defect region and the sound (undamaged) area. The result of the calculation of SNR can be seen in Table 2.  
The comparative analysis of SNR values for PCA and RPCA across various PT CFRP samples demonstrates that both methods achieve their optimal defect contrast in the first principal component (PC1). RPCA consistently provides slightly higher SNR than PCA for most samples, confirming its effectiveness in suppressing background noise. However, a clear anomaly is observed for the Thin 2.5 J sample, in this case, PCA outperforms RPCA.  
Table 3 summarizes how well both LVT and PT performed in detecting impact damage across various CFRP samples. For each case, the table presents the highest SNR value achieved by each technique, alongside representative thermal images that illustrate defect visibility.  
From the table, it is clear that PT consistently provides higher SNR values than LVT, sometimes by a considerable margin. For instance, in the Thin 2.5 J sample, the SNR for PT is more than two fold of SNR for the LVT (29.36 dB versus 14.13 dB), and this pattern persists across both thin and thick samples at different impact energies. The visual images reinforce this finding, defects generally appear sharper and more distinct in the PT images, whereas in LVT some defects are less obvious or harder to distinguish, particularly in samples with lower SNR.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **RPCA (SNR) (dB)** & **PCA SNR (dB)** & **Best PC** \\ \hline Thin 2.5 J & 28.62 & 29.36 & PC1 \\ Thin 4 J & 35.87 & 34.68 & PC1 \\ Thin 8A J & 42.11 & 40.36 & PC1 \\ Thin 8B J & 42.07 & 40.77 & PC1 \\ Thin 12 J & 38.63 & 37.97 & PC1 \\ Thick 4 J & not detected & not detected & not detected \\ Thick 8A J & 42.31 & 41.22 & PC1 \\ Thick 8B J & 35.01 & 34.58 & PC1 \\ Thick 12 J & 41.07 & 40.16 & PC1 \\ Thick 20 J & 49.16 & 47.56 & PC1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of RPCA and PCA SNR values (dB) for PT samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **LVT** & **LVT Image** & **PT** \\
**(Type + J)** & **SNR** & **SNR** & **PT Image** \\ \hline Thin 2.5 & 14.13 & 29.36 & \\ \hline Thin 4.0 & 23.13 & 35.87 & \\ \hline Thin 8.0 (A) & 28.12 & 42.11 \\ \hline Thin 8.0 (B) & 31.75 & 42.07 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of best PCs and defect visibility using LVT and PT.