###### Abstract  
The furniture manufacturing sector faces significant challenges in machining composite materials, where quality issues such as delamination can lead to substandard products. This study aims to improve the classification of drilled holes in melamine-faced chipboard using Explainable AI (XAI) techniques to better understand and interpret Convolutional Neural Network (CNN) models' decisions. We evaluated three CNN architectures (VGG16, VGG19, and ResNet01) pretrained on the ImageNet dataset and fine-tuned on our dataset of drilled holes. The data consisted of 8526 images, divided into three categories (Green, Yellow, Red) based on the drill's condition. We used 5-fold cross-validation for model evaluation and applied LIME and Grad-CAM as XAI techniques to interpret the model decisions. The VGG19 model achieved the highest accuracy of 67.03% and the lowest critical error rate among the evaluated models. LIME and Grad-CAM provided complementary insights into the decision-making process of the model, emphasizing the significance of certain features and regions in the images that influenced the classifications. The integration of XAI techniques with CNN models significantly enhances the interpretability and reliability of automated systems for tool condition monitoring in the wood industry. The VGG19 model, combined with LIME and Grad-CAM, offers a robust solution for classifying drilled holes, ensuring better quality control in manufacturing processes. Keywords:explainable AI (XAI); LIME; Grad-CAM; convolutional neural networks; CNN; drilled holes classification; melamine-faced chipboard; tool condition monitoring +
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substandard products. This is particularly true during the drilling operation, in which multiple factors might lead to undesirable results. In recent years, the application of machine learning and data augmentation techniques has emerged as a promising approach to address such challenges, especially when working with limited datasets. Capturing sufficient and balanced data for intelligent fault diagnosis remains a significant issue across various domains. For instance, recent research by [PERSON] et al. (2024) highlights how counterfactual-augmented few-shot contrastive learning can effectively identify faults even with limited and imbalanced samples by focusing on discriminative representations ([PERSON] et al., 2024). Similarly, the use of interpretable data-augmented adversarial variational autoencoders (AVAE) has been shown to improve diagnostic accuracy in imbalanced datasets, a common scenario in fault diagnosis tasks like those encountered in the wood processing industry ([PERSON] et al., 2024). These advancements underscore the importance of integrating such approaches into automated systems for tool condition monitoring in the wood industry, where consistent quality control is critical. Explainable Artificial Intelligence (XAI) has gained significant attention in recent years due to its potential to address the black-box problem associated with advanced deep learning models, especially in critical applications such as healthcare, finance, and industrial automation. XAI enables the interpretation of AI model outputs, fostering trust and transparency in AI systems, which are crucial for their broader adoption. [PERSON] et al. (2011) explored the application of Convolutional Neural Networks (CNNs) for partial discharge (PD) classification in cast-resin transformers. The study highlights the challenge posed by the lack of transparency in CNNs, which leads to difficulties in understanding and trusting model predictions. The authors propose the use of Grad-CAM, an XAI technique, to provide visual explanations for model decisions, thereby enhancing the reliability of PD classification systems. The research demonstrated a classification accuracy of approximately 97%, and the incorporation of XAI helped identify the criteria used by the model, ultimately leading to improved trust and robustness in the system. [PERSON] et al.