Tool wear monitoring using genetically-generated fuzzy knowledge bases

被引：36

作者：

Achiche, S

Balazinski, M

Baron, L

Jemielniak, K

机构：

[1] Ecole Polytech, Dept Engn Mech, Montreal, PQ H3C 3A7, Canada

[2] Warsaw Univ Technol, Fac Prod Engn, PL-02524 Warsaw, Poland

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2002年 / 15卷 / 3-4期

基金：

加拿大自然科学与工程研究理事会;

关键词：

artificial intelligence; fuzzy decision support system; knowledge base; tool condition monitoring; genetic algorithm;

D O I：

10.1016/S0952-1976(02)00071-4

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Fuzzy logic is an AI method that is being implemented in a growing number of different fields. One of these applications is tool wear monitoring. The construction of a fuzzy knowledge base from a set of experimental data by a human expert however, is a time consuming task, and hence, limits the expansion of the use of this AI method. Alternatively, the fuzzy knowledge base can be automatically constructed by a genetic algorithm from the same set of experimental data without requiring any human expert. This paper compares these two fuzzy knowledge base construction methods and the results obtained in a tool wear monitoring application. (C) 2002 Elsevier Science Ltd. All rights reserved.

引用

页码：303 / 314

页数：12

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