Paper Title:
Tool Wear Monitoring in Milling Processes Based on Time-Frequency Analysis of Acoustic Emission
  Abstract

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
Chapter
Chapter 3: Functional Manufacturing and Information Technology
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, G. F. Wang, X. D. Qin, X. L. Feng, "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
$32.00
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