## 5 Conclusions  
This study explored the application of Explainable AI (XAI) techniques, specifically LIME and Grad-CAM, to enhance the interpretability of Convolutional Neural Network (CNN) models used for the classification of drilled holes in melamine-faced chipboard. The primary focus was on understanding the decision-making process of these models to ensure reliability and improve classification accuracy.  
The research involved evaluating three pre-trained CNN architectures: VGG16, VGG19, and ResNet101. The performance of these models was assessed based on accuracy and critical error rates using a 5-fold cross-validation approach. The VGG19 model emerged as the best performer, achieving the highest average accuracy of 67.03% and the lowest critical error rate. This model was therefore selected for further analysis using XAI techniques.  
Both LIME and Grad-CAM were applied to the VGG19 model to generate explanations for its predictions. These techniques provided valuable insights into the features and regions of the images that influenced the model's classifications. LIME produced detailed, feature-level explanations, highlighting specific areas that contributed to the model's decisions. Grad-CAM, on the other hand, offered a more general overview, indicating broader regions that were important for the model's predictions.  
The comparison between LIME and Grad-CAM revealed that both methods generally agreed on the influential regions within the images, though LIME provided more granular details. In cases of misclassification, both techniques identified regions that misled the model, with some discrepancies attributed to the different approaches of the two methods. LIME's perturbation-based approach and Grad-CAM's gradient-based technique highlighted complementary aspects of the model's decision-making process.  
The study underscores the importance of using XAI techniques to interpret and validate the predictions of deep learning models in industrial applications. By understanding the factors influencing the model's decisions, it is possible to improve the reliability and robustness of automated systems for tool condition monitoring.  
Future research will focus on refining the VGG19 model further to enhance its accuracy and reduce critical errors. Additionally, exploring other XAI techniques and combining them with LIME and Grad-CAM could provide a more comprehensive understanding of model behavior. Implementing these improvements will contribute to more effective and trustworthy AI systems in the wood industry and beyond.  
In conclusion, the integration of XAI techniques with CNN models for the classification of drilled holes in melamine-faced chipboard has demonstrated significant potential. The insights gained from this study pave the way for more transparent and reliable AI applications in manufacturing and other fields where understanding model decisions is crucial.  
Conceptualization, J.K. and A.S.; methodology, J.K. and A.S.; software, J.K. and A.S.; validation, J.B. and A.J.; formal analysis, J.K.; investigation, J.K. and A.S.; resources, J.K.; data curation, A.J.; writing--original draft preparation, J.K.; writing--review and editing, J.K., A.S., A.J. and J.B.; visualization, J.B.; supervision, J.K.; project administration, J.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy and confidentiality.  
The authors declare no conflicts of interest.