Tool Wear Monitoring in Milling Processes Based on Time-Frequency Analysis of Acoustic Emission

Abstract:

Article Preview

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.

Info:

Periodical:

Edited by:

Hun Guo, Taiyong Wang, Zeyu Weng, Weidong Jin, Shaoze Yan, Xuda Qin, Guofeng Wang, Qingjian Liu and Zijing Wang

Pages:

574-577

DOI:

10.4028/www.scientific.net/AMM.141.574

Citation:

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

Online since:

November 2011

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Price:

$35.00

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