###### Abstract  
The paper presents the comparative analysis of Long short-term memory (LSTM) and one-dimensional convolutional neural networks (1-D CNNs) for tool wear classification in chipboard milling processes. The complexity of sequence data in various fields makes selecting the right model for sequence classification very important. This research aims to show the distinct capabilities and performance nuances of LSTM and 1-D CNN models, leveraging their inherent strengths in understanding temporal dependencies and feature extraction, respectively. Through a series of experiments, the study unveils that while both models demonstrate competencies in handling sequence data, the 1-D CNN model, with its superior feature extraction capabilities, achieved the best performance, boasting an accuracy of 94.5% on the test dataset. The insights gained from this comparison not only help to understand LSTM and 1-D CNN models better, but also open the door for future improvements in using neural networks for complex sequence classification challenges. tool condition monitoring (TCM); milling processes; tool wear classification; 1-D CNN; long short-term memory (LSTM); sequential features +
Footnote †: journal: Applied  
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Footnote 1: email: _Applied_  
Footnote †: journal: Applied  
production and offers both accuracy and ease of implementation. This paper introduces a new solution that uses sensor-based data to address these challenges. We can use machine learning (ML) to monitor the condition of tools in manufacturing or even deep learning methods based sequential data. By applying ML/DL algorithms to image and sensor-based systems [16; 19; 20; 21; 22], we can improve how we track the condition of tools. This approach shows that ML can handle even the toughest tasks if we have the right data and training methods. When it comes to monitoring a tool's condition, the type of data you use--whether it is images or signals--is really important. Using images with convolutional neural networks (CNNs) [1; 23; 24] has its advantages, but it requires a lot of consistent training data and strict data collection. On the other hand, using signals for monitoring is precise, but managing and processing the large volume of varied sensor data can be challenging. Traditional approaches in tool wear classification have often relied on singular value features extracted from sensor data, such as the mean, standard deviation, or maximum values within a given aspect, e.g., time window. While useful, these methods can overlook the rich temporal dynamics inherent in sequential data, which can be crucial for accurately predicting tool wear. This paper explores the shift towards leveraging sequential features for tool condition monitoring [25; 26; 27; 28], emphasizing the advantages of this approach over traditional singular value feature extraction methods. Singular value features, though easy to compute and interpret, may not fully capture the intricate behavior of tools under varying operational conditions. Such features tend to provide a snapshot rather than a continuous narrative of the tool's wear process, potentially leading to the oversimplification of complex phenomena. This limitation becomes particularly evident in scenarios where the temporal progression of wear significantly influences the tool's performance and life expectancy. Sequential features, in contrast, offer a more nuanced and comprehensive representation of tool wear. By preserving the temporal order and dependencies within the data, sequential features enable the modeling of wear progression over time, providing valuable insights into the tool's condition and the dynamics of machining processes. This approach allows for a more detailed and accurate analysis, facilitating early detection of wear and more informed decision-making regarding tool maintenance and replacement. The extraction of sequential features involves analyzing sensor data over time to identify patterns, trends, and anomalies that may indicate tool wear.