Comparison of applying static and dynamic features for drill wear prediction

被引：5

作者：

Xu, Jie ^{[1
]}

Yamada, Keiji ^{[1
]}

Seikiya, Katsuhiko ^{[1
]}

Tanaka, Ryutaro ^{[1
]}

Yamane, Yasuo ^{[1
]}

机构：

[1] Hiroshima Univ, Grad Sch Engn, Higashihiroshima, Hiroshima 7398527, Japan

来源：

JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING | 2014年 / 8卷 / 04期

关键词：

Drill wear prediction; Wavelet packet transform; Back propagation neural network; ARTIFICIAL NEURAL-NETWORKS; FORCE;

D O I：

10.1299/jamdsm.2014jamdsm0056

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper defines static and dynamic component parameters based on the method that converts thrust and torque detected during drilling process into equivalent thrust force and principal force. Features of the parameters are extracted by wavelet packet transform (WPT) and then used to train a back propagation neural network (BPNN) to predict the drill wear. Experiments with different drilling conditions and workpiece materials were conducted and it has been confirmed that both static and dynamic component parameters are affected by the drilling conditions. The features extracted from dynamic components in lower frequency band can predict the drill corner wear better.

引用

页数：8

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