Abstract
In this paper, vibration signals are collected from a milling machine including cutting conditions with different cutting parameters. The multiple features are extracted from the measured signals to describe the cutting conditions. As feature selection methods, principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are utilized to reduce the redundant information in the multiple features. K-Nearest Neighbor (KNN) classifier is employed to evaluate the capability of these feature selection methods. The experimental results show that the features processed by manifold learning could improve the performance of KNN. And the limitation of t-SNE applied to cutting condition classification is discussed. The manifold learning t-SNE could give a potential way to construct effective cutting condition classification systems.
Original language | English |
---|---|
Article number | 055003 |
Journal | Proceedings of Meetings on Acoustics |
Volume | 42 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | 179th Meeting of the Acoustical Society of America, ASA 2020 - Virtual, Online Duration: Dec 7 2020 → Dec 11 2020 |
Bibliographical note
Publisher Copyright:© 2021 Acoustical Society of America.
ASJC Scopus Subject Areas
- Acoustics and Ultrasonics