## 4 Conclusions  
In this study, an ultrasonic pulse-echo method integrated with the AGG-16 convolutional neural network was developed to detect defects in adhesive bonds and to automate the process. In the post-processing step of the collected experimental data, where the signals were aligned according to the surface reflection time, the surface curvature factor influencing the detection was reduced. However, the systematic amplitude variation at the interface remained and could not be fully mitigated. Therefore, a significant improvement in defect detection accuracy was achieved by integrating the VGG-16 CNN. The results obtained showed that the VGG-16 CNN was able to effectively learn and recognize complex patterns in the C-scan images, demonstrating its capability to adapt to non-ideal inspection conditions and automate the entire process reducing the time required. The main results of the using the VGG-16 network to classify defective and non-defective areas are as follows:  
1. The VGG-16 model provided a high classification accuracy rate of 98.95% for true positive cases and 99.65% for true negative cases on the training dataset, indicating a strong ability to distinguish between defective and non-defective adhesive bonds;
2. Low false positive and negative rates were obtained during model training, at 1.05% and 0.35%, respectively, indicating a high level of precision and reliability;
3. Robust performance of the model was achieved when trialed on \"unseen\" data, highlighting its potential for generalization across different samples. Although the model's performance slightly declined with unseen data, its reliable classification ability was demonstrated with true positive and true negative rates of 92.31% and 86.54%, respectively;
4. The false positive and negative rates obtained on the \"unseen\" data remained within 15%, equaling 13.46% and 7.69%, respectively, which is still reasonably strong and acceptable in many practical applications;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Metrics** & **Test Data** & **Unseen Data** \\ \hline Accuracy & 0.99 & 0.89 \\ Precision & 0.98 & 0.92 \\ Recall & 0.99 & 0.87 \\ F1-score & 0.99 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Additional performance metrics on the test and unseen data.  
5. The tendency to overestimate non-defective cases as defects rather than miss actual defects makes the model well-suited to applications where safety is paramount.  
As a result, this study demonstrates that the integration of advanced neural network techniques with traditional ultrasonic inspection methods can significantly enhance detection capabilities within adhesive bonds, providing a valuable tool for ensuring the integrity and safety of critical structural components.  
Conceptualization, V.S. and E.J.; methodology, V.S., E.J. and D.S.; software, V.S. and D.S.; validation, V.S.; formal analysis, V.S. and D.S.; investigation, V.S., E.J. and D.S.; resources, V.S., E.J. and D.S.; data curation, D.S. and V.S.; writing--original draft preparation, D.S. and V.S.; writing--review and editing, E.J. and V.S.; visualization, D.S. and V.S.; supervision, V.S. and E.J.; project administration, V.S. and E.J.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research Council of Lithuania (LMTLT), agreement no. S-PD-22-25 and agreement no. S-MIP-22-5.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.