Tool condition monitoring by SVM classification of machined surface images in turning

被引：56

作者：

Bhat, Nagaraj N. ^{[1
]}

Dutta, Samik ^{[2
]}

Vashisth, Tarun ^{[3
]}

Pal, Srikanta ^{[4
]}

Pal, Surjya K. ^{[5
]}

Sen, Ranjan ^{[2
]}

机构：

[1] BIT, Dept Elect & Elect Engn, Ranchi 835215, Bihar, India

[2] CSIR Cent Mech Engn Res Inst, Precis Engn & Metrol Grp, Durgapur, India

[3] BIT, Dept Elect & Commun Engn, Ranchi 835215, Bihar, India

[4] Shiv Nadar Univ, Dept Elect Engn, Gautam Budh Nagar 201314, Uttar Pradesh, India

[5] Indian Inst Technol, Dept Mech Engn, Kharagpur 721302, W Bengal, India

来源：

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2016年 / 83卷 / 9-12期

关键词：

Feature selection; Turning; GLCM; Surface texture; Fisher discriminant analysis; Tool condition monitoring; Support vector machines; SUPPORT VECTOR MACHINE; LEAST-SQUARES; VISION SYSTEM; ROUGHNESS; TEXTURE; WEAR; PREDICTION; FEATURES; SIGNALS; MODEL;

D O I：

10.1007/s00170-015-7441-3

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Tool condition monitoring has found its importance to meet the requirement of quality production in industries. Machined surface is directly affected by the extent of tool wear. Hence, by analyzing the machined surface, the information about the cutting tool condition can be obtained. This paper presents a novel technique for multi-classification of tool wear states using a kernel-based support vector machine (SVM) technique applied on the features extracted from the gray-level co-occurrence matrix (GLCM) of machined surface images. The tool conditions are classified into sharp, semi-dull, and dull tool states by using Gaussian and polynomial kernels. The proposed method is found to be cost-effective and reliable for online tool wear classification.

引用

页码：1487 / 1502

页数：16

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