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

被引：0

作者：

Zhang, Lu ^{[1
]}

Wang, Guofeng ^{[1
]}

Qin, Xuda ^{[1
]}

Feng, Xiaoliang ^{[1
]}

机构：

[1] Tianjin Univ, Sch Mech Engn, Tianjin 300072, Peoples R China

来源：

FUNCTIONAL MANUFACTURING AND MECHANICAL DYNAMICS II | 2012年 / 141卷

关键词：

Tool wear monitoring; Time-frequency analysis; Support vector machine; Acoustic emission;

D O I：

10.4028/www.scientific.net/AMM.141.574

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：574 / 577

页数：4

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