Tool condition monitoring based on numerous signal features

被引：89

作者：

Jemielniak, Krzysztof ^{[1
]}

Urbanski, Tomasz ^{[1
]}

Kossakowska, Joanna ^{[1
]}

Bombinski, Sebastian ^{[1
]}

机构：

[1] Warsaw Univ Technol, Fac Prod Engn, PL-02534 Warsaw, Poland

来源：

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2012年 / 59卷 / 1-4期

关键词：

Tool condition monitoring; Signal feature selection; Aerospace material;

D O I：

10.1007/s00170-011-3504-2

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a tool wear monitoring strategy based on a large number of signal features in the rough turning of Inconel 625. Signal features (SFs) were extracted from time domain signals as well as from frequency domain transforms and their wavelet coefficients (time-frequency domain). All of them were automatically evaluated regarding their relevancy for tool wear monitoring based on a determination coefficient between the feature and its low-pass-filtered course as well as the repeatability. The selected SFs were used for tool wear estimation. The accuracy of this estimation was then used to evaluate the sensor and signal usability.

引用

收藏

页码：73 / 81

页数：9

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