TSK fuzzy modeling for tool wear condition in turning processes: An experimental study

被引：31
|
作者：
Ren, Qun [1 ]
Balazinski, Marek [1 ]
Baron, Luc [1 ]
Jemielniak, Krzysztof [2 ]
机构：
[1] Ecole Polytech, Dept Mech Engn, Montreal, PQ H3C 3A7, Canada
[2] Warsaw Univ Technol, Fac Prod Engn, PL-02524 Warsaw, Poland
来源：
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2011年 / 24卷 / 02期
关键词：
TSK fuzzy modeling; Tool wear condition; Subtractive clustering; IDENTIFICATION; SYSTEMS; RULES;
D O I：
10.1016/j.engappai.2010.10.016
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 an experimental study for turning process in machining by using Takagi-Sugeno-Kang (TSK) fuzzy modeling to accomplish the integration of multi-sensor information and tool wear information. It generates fuzzy rules directly from the input-output data acquired from sensors, and provides high accuracy and high reliability of the tool wear prediction over a wide range of cutting conditions. The experimental results show its effectiveness and satisfactory comparisons relative to other artificial intelligence methods. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码：260 / 265
页数：6
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