###### Abstract  
This evaluation of deep learning and traditional machine learning methods for tool state recognition in milling processes aims to automate furniture manufacturing. It compares the performance of long short-term memory (LSTM) networks, support vector machines (SVMs), and boosting ensemble decision trees, utilizing sensor data from a CNC machining center. These methods focus on the challenges and importance of feature selection, data preprocessing, and the application of tailored machine learning models to specific industrial tasks. Results show that SVM, with an accuracy of 96%, excels in handling high-dimensional data and robust feature extraction. In contrast, LSTM, which is appropriate for sequential data, is constrained by limited training data and the absence of pre-trained networks. Boosting ensemble decision trees also demonstrate efficacy in reducing model bias and variance. Conclusively, selecting an optimal machine learning strategy is crucial, depending on task complexity and data characteristics, highlighting the need for further research into domain-specific models to improve performance in industrial settings. boosting ensemble decision trees; long short-term memory (LSTM); support vector machine (SVM); tool condition monitoring (TCM); time series analysis +
Footnote †: journal: Applied Sciences  
While the sensor-based approach provides some interesting possibilities, the subject of tool condition monitoring is not new and remains a widely discussed one [7; 8]. The edge of the tool gradually deteriorates during the process, which can result in decreased product quality. It is important to note that two situations need to be avoided in this case: unnecessarily stopping the production process and delaying the exchange past the point when the results start to be unacceptable. Bot situations can generate a loss for the company (either due to time or wasted materials), and any automatic solution should consider those. The decision-making process needs to be as precise as possible while providing interactive, insightful feedback for the operator. Incorporating specialized sensors in the process seems to be the best approach in that aspect. While numerous works focus on similar problems, different signals are used, depending on the chosen tasks. The subsequent evaluation checks and verifies their usefulness in identifying tool conditions at different stages of the machining process. Additionally, even though extensive studies cover the problem, there is still a significant need for an automatic and precise solution that is easy to incorporate into the production process in the actual work environment. One of the significant innovations presented in this paper focuses on a way to solve that problem using sensor-based data. Since the overall task is complex, applying machine learning (ML) to it seems to be the best approach. The applicability of ML algorithms and the importance of such solutions in manufacturing has grown in recent years. There are numerous examples of different algorithms being applied to similar tasks in image and sensor-based systems [9; 10]. The work presented in this paper applies those techniques to tool condition monitoring, extending the overall scope with a novel approach to the task. Depending on the chosen approach, different aspects and applications are considered. At the same time, solutions such as tree species recognition, as shown in [11] show the vast possibilities and adaptability that ML offers. Even the most complicated tasks can be solved, assuming appropriate input data and a well-designed training workflow. When specifically considering tool condition monitoring, the main distinction concerns the parts used. Some existing solutions use images instead of signals, often pairing such input with convolutional neural networks (CNNs) [1; 2; 12; 13]. The training process itself can be improved by using pre-trained networks (such as AlexNet [14; 15] prepared for the ImageNet database [16; 17]) or data augmentation.