Comparative Study of Conventional Machine Learning versus Deep Learning-Based Approaches for Tool Condition Assessments in Milling Processes

被引:0
|
作者
Przybys-Malaczek, Agata [1 ]
Antoniuk, Izabella [1 ]
Szymanowski, Karol [2 ]
Kruk, Michal [1 ]
Sieradzki, Alexander [1 ]
Dohojda, Adam [1 ]
Szopa, Przemyslaw [1 ]
Kurek, Jaroslaw [1 ]
机构
[1] Warsaw Univ Life Sci, Inst Informat Technol, Dept Artificial Intelligence, PL-02776 Warsaw, Poland
[2] Warsaw Univ Life Sci, Inst Wood Sci & Furniture, Dept Mech Proc Wood, PL-02776 Warsaw, Poland
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 13期
关键词
boosting ensemble decision trees; long short-term memory (LSTM); support vector machine (SVM); tool condition monitoring (TCM); time series analysis; EMPIRICAL MODE DECOMPOSITION;
D O I
10.3390/app14135913
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页数:21
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