###### Abstract
In this research, the study relies on photographs of drilled holes as the primary dataset, working from the premise that the condition of the drill edges can reveal the wear on the tool, thereby indicating the appropriate timing for tool replacement. This theory has yielded encouraging outcomes through the application of various techniques and algorithms. Techniques based on deep learning and transfer learning have demonstrated efficiency and a strong alignment with this specific challenge [39; 40; 41; 42; 43]. Additional investigations propose that the implementation of classifier ensembles and data augmentation techniques could improve the overall performance [44; 45], with various classifiers being evaluated and showing considerable potential for real-world usage. Explainable AI (XAI) techniques, such as Class Activation Mapping (CAM), have been applied to identify points of concern in image-based Deep Neural Networks (DNNs) used for failure analysis in industrial applications [46; 47; 48; 49; 50]. The study highlights the importance of XAI tools in providing insights into the decision process of DNNs and emphasizes the need for further analysis of their correctness. Local Interpretable Model-agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (GRAD-CAM) have shown their utility in various industrial applications. CNN and GRAD-CAM techniques have been utilized in detecting defects and pinpointing their locations on industrial products. However, issues related to accurately identifying defective regions and the time required for processing have been noted [51; 52; 53; 54]. Additionally, the application of LIME in the automated detection of fractures during material testing has been explored, offering insights into the capability for critical feature detection and the automation of crack detection. Challenges in drill state recognition have been addressed by transfer learning using CNN, offering the advantage of training classification models with a small portion of data [19]. Data augmentation techniques combined with transfer learning have improved the accuracy of drill wear recognition, even with a small original dataset [20]. Despite the absence of direct information on the specific use of LIME and GRAD-CAM for drilled hole classification in melamine-faced chipboard, the insights from XAI techniques and their application in industrial contexts suggest their potential applicability. LIME and GRAD-CAM are valuable XAI techniques that provide insights into the decision-making process of CNN-based classification models for drilled holes in melamine-faced chipboard. These methods provide benefits regarding the accuracy of explanations and the visual representation of specific features. However, there are drawbacks that require further investigation in upcoming studies. This paper explores the use of LIME and GRAD-CAM for enhancing the interpretability of CNN-based models in classifying drilled holes in melamine-faced chipboard. By applying these XAI techniques, we aim to better understand the model's decision-making process, ensure its reliability, and improve the overall accuracy of the classification.