## 4 Results and Discussion of Explainable AI (XAI) for VGG19 Model  
In this section, we discuss the application of two Explainable AI (XAI) techniques, namely Local Interpretable Model-agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM), to the VGG19 model. The aim is to interpret the model's decisions and identify the most influential features contributing to the classification of drilled holes in melamine-faced chipboard. LIME Results  
Figure 5 shows LIME-generated explanations for correctly classified VGG19 model's predictions on images of drilled holes. The top row (a-c) shows the original images, and the bottom row (d-f) shows the corresponding LIME explanations:  
* (a) Actual class: Green, predicted class: Green. The LIME analysis in (d) underscores the sections of the picture that had the most significant impact on the model's identification of the \"Green\" class. * (b) Actual class: Yellow, predicted class: Yellow. The LIME explanation in (e) indicates the areas that influenced the model's decision to classify the image as \"Yellow\". * (c) Actual class: Red, predicted class: Red. The LIME explanation in (f) shows the key features that led the model to correctly identify the image as belonging to the \"Red\" class. Figure 6 shows LIME-generated explanations for misclassified VGG19 model's predictions on images of drilled holes. The top row (a-c) shows the original images, and the bottom row (d-f) shows the corresponding LIME explanations:  
* (a) Actual class: Green, predicted class: Red. The LIME explanation in (d) highlights the regions of the image that contributed most to the model's prediction of the \"Red\" class. Based on the explanation image, it is evident that the actual condition should be Red instead of Green, and the VGG19 model predicted correctly (66% for the red class). However, the problem is that sometimes even with a good drill bit, damages (artifacts in the image) can occur due to local poor quality of the material or other issues not resulting from drill bit wear. * (b) Actual class: Green, predicted class: Red. The LIME explanation in (e) indicates the areas that influenced the model's decision to classify the image as \"Red\". Similarly,in this case, the state according to the image should be Red/Yellow. The model decided on the Red class (50% for red, 49% for yellow) on the boundary. * (c) Actual class: Red, predicted class: Green. The LIME explanation in (f) shows the key features that led the model to identify the image as belonging to the \"Green\" class. In this case, the problem lies with the model; it should have assigned the Red/Yellow class. However, it shows 58% for the green class and 32% for the yellow class, and only 10% for the Red class. The issue might be the greater number of artifacts at the bottom left of the drilled hole. Grad-CAM Results  
Figure 11 shows Grad-CAM-generated explanations for correctly classified VGG19 model's predictions on images of drilled holes. The top row (a-c) shows the original images, and the bottom row (d-f) shows the corresponding Grad-CAM explanations:  
* (a) Actual class: Green, predicted class: Green. The Grad-CAM explanation in (d) highlights the regions of the image that contributed most to the model's prediction of the \"Green\" class. * (b) Actual class: Yellow, predicted class: Yellow. The Grad-CAM explanation in (e) indicates the areas that influenced the model's decision to classify the image as \"Yellow\". * (c) Actual class: Red, predicted class: Red. The Grad-CAM explanation in (f) shows the key features that led the model to correctly identify the image as belonging to the \"Red\" class. Figure 11: Explanations generated by Grad-CAM for the VGG19 model’s correct classifications of images showcasing drilled holes are presented.