## 3 Results and Discussion  
Adhesively bonded lap joints of aluminum and CFRP with disbond-type defects were investigated using an ultrasonic NDT method integrated with a CNN. The primary objective of this study was to improve the performance of the traditional ultrasonic pulse-echo inspection method for detecting interface defects in adhesive bonded joints, which are influenced by surface curvature that is not visually observable. The impact of this curvature cannot be fully eliminated using traditional inspection and processing methods and significantly affects defect detection. Another goal was to automate the process to reduce the time required for investigation and analysis. Therefore, the VGG-16 convolutional neural network was utilized in conjunction with the pulse-echo ultrasonic method. The integration of the VGG-16 CNN with the pulse-echo ultrasonic method not only improved defect detection but also allowed for automation of the entire workflow. Data preprocessing involved filtering and aligning signals by surface reflection, followed by the automatic extraction of multiple C-scan images at different time instances and training the VGG-16 network. This automation replaced manual feature extraction and data analysis, significantly reducing the time required for inspection. We started from the main challenge, which involved systematic amplitude variations that complicate interface defect detection. To address this, C-scan images were generated at specific time moments, capturing variations over time. By automating the extraction of C-scan images at specific time moments and feeding them into the CNN, the need for manual examination by an operator was eliminated, greatly reducing the time and effort required to analyze the data. The CNN was able to process large amounts of data and classify regions as defective or non-defective with minimal operator intervention. The VGG-16 network was chosen for its ability to learn and recognize complex patterns and non-linear relationships from these images. The neural network model was tested using the validation data used in the model training process and using the unseen data, acquired from Sample No. 2 (through the independent experiment, as described in Section 2.1). The datasets were then fed into the neural network, and training was performed using the  
Figure 8: The obtained neural network accuracy and losses after 22 epochs of training and validation. Adam optimizer. Once trained, the VGG-16 model was capable of analyzing thousands of C-scan images quickly and efficiently, avoiding time-consuming manual interpretation. The results, including the train/validation performance and results for the \"unseen\" data, are presented in the confusion matrix **C**. The matrix consists of the following components: **C\({}_{\textbf{0,0}}\)** for true positive, **C\({}_{\textbf{1,0}}\)** for false positive, **C\({}_{\textbf{1,1}}\)** for true negative, and **C\({}_{\textbf{0,1}}\)** for false negative. These values correspond to the accuracy of correctly and incorrectly classified observations. Thus, the model's classification accuracy, generalization capabilities, and ability to detect subtle variations in defective and non-defective areas were assessed. The results of \"defective\" versus \"non-defective\" classifiers of the trained VGG-16 model are presented in Figure 9.  
The results presented in Figure 9 demonstrate that the model correctly identified 98.95% of true positive cases, accurately detecting the presence of defects, and 99.65% of true negative cases, where the model correctly classified the absence of defects, indicating a perfect bond. The false negative rate (defect oversight) was 1.05%, and the false positive rate (defect overestimation) was 0.35% on the training data, indicating a high level of precision and reliability during the training phase. When applied to the \"unseen\" dataset (Figure 9b), the model's performance slightly declined, with the true positive rate dropping to 92.31% and the true negative rate to 86.54%.