## 3 Results and Discussion
Consequently, 13.46% of samples were incorrectly classified as defective, while 7.69% of actual defects were missed (classified as non-defective). While this represents a reduction in both sensitivity and specificity, it is important to consider that the training dataset was relatively limited, and the unseen data was acquired from a different sample with varying alumina and CFRP thicknesses. The unseen sample, sourced from a different manufacturer to the first sample, i.e., FL Technics in Lithuania, was specifically chosen to represent a different manufacturing process and adhesive bonding conditions compared to the Sample No.1 manufactured in COTESA GmbH, Mittweida, Germany. The unseen sample allowed us to simulate the variability in sample and bonded plate dimensions, defect sizes, environmental conditions, and interface patterns. This variability in the unseen sample reflects the range of conditions that may be encountered in real-world applications, enhancing the model's relevance and applicability in practical scenarios. Given these factors, the model's generalization and prediction capacity can be regarded as reasonably strong, particularly since both false positive and false negative rates remained within 15%, a threshold generally considered acceptable in many practical applications. Although the model showed an overall decrease in sensitivity and specificity when applied to unseen data, it is noteworthy to mention that the false positive rate (13.46%) was higher than the false negative rate (7.69%). This is generally considered favorable, as the model tends to overestimate non-defective cases as defects  
Figure 9: Confusion matrices of “defective” and “non-defective” classification using the VGG-16 model. (**a**) Confusion matrix for the train/validation data; (**b**) Confusion matrix for predictions on “unseen” experimental data. rather than miss actual defects, an important quality in safety-critical industries, where overlooking defects can pose significant risks. In addition to the confusion matrix presented in Figure 9, further performance metrics were calculated to provide a more comprehensive evaluation of the VGG-16 model's effectiveness. The accuracy, precision, recall, and F1-scores obtained on the test and unseen data are presented in Table 1.  
While the model's performance decreased in response to unseen data, its tendency to err on the side of caution by overestimating defects rather than missing them makes it well-suited for applications where safety is a primary concern. To further improve its performance, especially in handling borderline cases, several strategies can be considered. Firstly, fine-tuning the decision threshold, or applying cross-validation and regularization techniques, may help achieve a better balance between sensitivity and specificity, allowing for a more favorable trade-off between minimizing missed defects and controlling the overestimation of defects. Secondly, enhancing the training dataset with more diverse samples could significantly improve the model's robustness and generalization. Including data from samples with varying thicknesses, different experimental conditions, and diverse defect characteristics would provide the model with a broader range of scenarios, enabling it to handle unseen data even more effectively.