###### Abstract
(2012) focused on leveraging XAI not only for interpretability, but also to enhance the performance of machine learning models. The authors assessed well-known XAI techniques, such as Integrated Gradients, in the context of classification tasks using datasets like Fashion-MNIST, CIFAR10, and STL10. The study revealed that XAI methods could be used strategically to identify critical features that directly contribute to improving model accuracy. This approach suggests that XAI can serve as a dual-purpose tool--both for interpretability and performance enhancement. In another study, [PERSON] et al. (2013) applied XAI to light direction reconstruction models used in augmented reality. The study emphasized the caution needed when interpreting XAI outputs due to potential unreliability. Nevertheless, by carefully leveraging the meta-information provided by XAI methods, the authors were able to enhance the architecture and training dataset of a regression model, leading to improved performance. This work underscores the role of XAI in guiding the optimization of model design and training. [PERSON] et al. (2014) introduced Grad-CAM, a widely used XAI technique that produces visual explanations for CNN-based models by highlighting relevant regions in input images. Grad-CAM is applicable to various CNN architectures, and has demonstrated its utility across multiple tasks, including image classification, captioning, and visual question answering (VQA). By combining Grad-CAM with fine-grained visualizations, the study achieved superior model interpretability and generalization, addressing key challenges such as adversarial robustness and dataset bias. [PERSON] et al. (2015) presented a comprehensive review of the XAI field, providing a taxonomy of existing methods and highlighting challenges in achieving responsible AI. The review emphasizes the significance of explainability in machine learning models, particularly in deep learning, where the complexity of models often obscures their decision-making processes. The authors propose a framework for responsible AI that integrates fairness, transparency, and accountability, paving the way for the large-scale implementation of AI in real-world applications. These studies collectively underscore the critical role of XAI in enhancing the transparency, reliability, and performance of AI models. The integration of XAI not only aids in the interpretability of complex models, but also provides avenues for improving model performance through more informed architectural and training choices. The foundational work in tool condition monitoring dates back nearly 40 years to [16; 17; 18], focusing on identifying reliable signals and features for detecting tool wear during production without downtime. This area of research has evolved, applying various approaches [19; 20; 21; 22; 23; 24; 25; 26; 27], typically involving multiple sensors to detect changes in physical parameters from the machining zone. Conventional methods usually require an array of sensors, such as acoustic emission, vibration testing, noise measurement, or evaluating cutting torque and feed force [28; 29; 30; 31; 32; 33]. While these methods can be accurate, they are often complex and time-consuming, requiring careful sensor placement and calibration. Initial setup errors can lead to unstable and inaccurate results, especially if regular folding of sensors is necessary. These drawbacks make sensor-based approaches challenging, particularly in dynamic production environments where conditions frequently change. Given the multitude of factors influencing drilling quality, machine learning (ML) algorithms emerge as a logical solution. In recent years, ML methods have gained traction in the wood industry for various applications, from monitoring machining processes [34; 35; 36; 37; 38] to wood species recognition. Tailored solutions or adaptable ML algorithms are available for specific problems.