## 4 Results and Discussion of Explainable AI (XAI) for VGG19 Model
### Comparison of LIME and Grad-CAM Explanations for VGG19
#### 4.1.5 Discussion of Yellow Class Misclassifications  
The misclassification of instances belonging to the \"Yellow\" class deserves special attention, as it represents an intermediate state in the condition of drilled holes. The \"Yellow\" category is particularly challenging to classify, due to its transitional nature between the \"Green\" (good) and \"Red\" (requiring replacement) classes. This ambiguity introduces uncertainties in both model predictions and human assessments. One of the primary challenges in accurately identifying the \"Yellow\" class lies in its inherent variability. Unlike the \"Green\" and \"Red\" classes, which have more clearly defined characteristics (new or significantly worn drill bits, respectively), the \"Yellow\" class represents a range of wear levels that are subject to interpretation. Factors such as minor surface defects, slight roughness around the edges of the drilled hole, or localized material inconsistencies can cause confusion between adjacent classes. The CNN models tested, including VGG16, VGG19, and ResNet101, frequently struggled with distinguishing \"Yellow\" from either \"Green\" or \"Red\". As seen in the confusion matrices, a substantial proportion of \"Yellow\" instances were misclassified as either \"Green\" or \"Red\". This tendency was particularly noticeable in cases where the visual characteristics of \"Yellow\" overlapped with those of the other two classes. These misclassifications are not unexpected, considering the visual continuity in the degradation of drill bit performance over time. From an industrial perspective, the misclassification of \"Yellow\" instances as \"Green\" or \"Red\" can have different implications. If a \"Yellow\" hole is misclassified as \"Green\", there is a risk of continuing to use a drill bit that is approaching its wear limit, potentially leading to quality issues in subsequent operations. On the other hand, misclassifying a \"Yellow\" hole as \"Red\" could lead to premature replacement of a drill bit, resulting in unnecessary downtime and increased operational costs. To reduce the frequency of these misclassifications, several strategies could be explored:  
* Improved data labeling. One approach involves refining the labeling process to better capture the nuances within the \"Yellow\" class. Introducing subcategories or gradations within the \"Yellow\" class might help the model learn the subtle differences between moderate wear levels. * Enhanced feature extraction. Incorporating additional feature extraction techniques, such as edge detection or texture analysis, could improve the model's ability to distinguish between \"Yellow\" and other classes. These features could provide more detailed information about the quality of the drilled holes, leading to more accurate classifications. * Artifact removal based on LIME/Grad-CAM explanations. Analysis using LIME and Grad-CAM can identify specific artifacts in the images that lead to misclassifications. By focusing on these artifacts and either removing them from the dataset or adjusting the image preprocessing pipeline to mitigate their effects, the model's performance can be improved, reducing the likelihood of incorrect class assignments. * Ensemble methods. Using ensemble models that combine the predictions of multiple CNN architectures may help to mitigate misclassifications. By aggregating the outputsof different models, it is possible to achieve a more balanced and reliable classification, especially for ambiguous cases. Human-in-the-loop systems. Implementing a hybrid system where \"Yellow\" classifications are verified by human experts could provide a safety net for cases where the model's confidence is low. This approach allows for continuous improvement through iterative feedback and model retraining. Further research should focus on understanding the visual and contextual factors that contribute to \"Yellow\" misclassifications.