Drilling Wear Recognition based on Fuzzy C-means Clustering Algorithm

被引：0

作者：

Yan, Mingxia ^{[1
]}

机构：

[1] Hubei Univ Technol, Mech Engn Coll, Wuhan 430068, Hubei, Peoples R China

来源：

MATERIALS PROCESSING TECHNOLOGY II, PTS 1-4 | 2012年 / 538-541卷

关键词：

Fuzzy c-means clustering; Matlab; Drilling Wear; Fault Diagnosis;

D O I：

10.4028/www.scientific.net/AMR.538-541.1408

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Fuzzy c-means clustering algorithm was introduced in detail to classify a set of original sampling data on drilling wear in this paper. Simulation results by Matlab programming show that drill wear modes can be successfully represented by four fuzzy grades after fuzzy clustering and classification. The analysis result indicates that fuzzy description can properly reflect drill wear, FCM can effectively identify different wear modes. It is suggested that the severe degree of membership of wear be used as a criterion for replacement of a drill. This technique is simple and is adaptable to different environment in automatic manufacturing.

引用

页码：1408 / 1412

页数：5