Affiliation:
1. Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland
2. Department of Mechanical Processing of Wood, Institute of Wood Sciences and Furniture, Warsaw University of Life Sciences, 02-776 Warsaw, Poland
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.