Tool condition monitoring techniques in milling process - a review

被引：234

作者：

Mohanraj, T. ^{[1
]}

Shankar, S. ^{[2
]}

Rajasekar, R. ^{[3
]}

Sakthivel, N. R. ^{[1
]}

Pramanik, A. ^{[4
]}

机构：

[1] Amrita Vishwa Vidyapeetham, Dept Mech Engn, Amrita Sch Engn, Coimbatore, Tamil Nadu, India

[2] Kongu Engn Coll, Dept Mechatron Engn, Erode, India

[3] Kongu Engn Coll, Dept Mech Engn, Erode, India

[4] Curtin Univ, Sch Civil & Mech Engn, Bentley, WA, Australia

来源：

JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T | 2020年 / 9卷 / 01期

关键词：

Milling process; Tool condition monitoring system; Sensor fusion; Feature extraction; Machine learning algorithm; ACOUSTIC-EMISSION; NEURAL-NETWORK; CUTTING FORCE; SENSOR FUSION; FLANK WEAR; PREDICTION; MACHINE; SIGNALS; SYSTEM; TEMPERATURE;

D O I：

10.1016/j.jmrt.2019.10.031

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The most important improvement in metal the cutting industry is the continuous utilization of cutting tools and tool condition monitoring system. In the metal cutting process, the tool condition has to be administered either by operators or by online condition monitoring systems to prevent damage to both machine tools and workpiece. Online tool condition monitoring system is highly essential in modern manufacturing industries for the rising requirements of cost reduction and quality improvement. This paper summaries various monitoring methods for tool condition monitoring in the milling process that have been practiced and described in the literature. (C) 2019 Published by Elsevier B.V.

引用

页码：1032 / 1042

页数：11

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