Tool Wear Monitoring in Milling Processes Based on Time-Frequency Analysis of Acoustic Emission
Tool wear monitoring plays an important role in the automatic machining processes. Therefore, it is necessary to establish a reliable method to predict tool wear status. In this paper, features of acoustic emission (AE) extracted from time-frequency domain are integrated with force features to indicate the status of tool wear. Meanwhile, a support vector machine (SVM) model is employed to distinguish the tool wear status. The result of the classification of different tool wear status proved that features extracted from time-frequency domain can be the recognize-features of high recognition precision.
Hun Guo, Taiyong Wang, Zeyu Weng, Weidong Jin, Shaoze Yan, Xuda Qin, Guofeng Wang, Qingjian Liu and Zijing Wang
L. Zhang et al., "Tool Wear Monitoring in Milling Processes Based on Time-Frequency Analysis of Acoustic Emission", Applied Mechanics and Materials, Vol. 141, pp. 574-577, 2012