Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review

In Computer Numerical Control (CNC) machining, determining optimum or appropriate cutting parameters can minimize machining errors such as tool breakage, tool deflection and tool wear, thus yielding a high productivity or minimum cost. There have been a number of attempts to determine the machining parameters through off-line adjustment or on-line adaptive control. These attempts use many different kinds of techniques: CAD-based approaches, Operations Research approaches, and Artificial Intelligence (AI) approaches. After describing an overview of these approaches, we will focus on reviewing Al-based techniques for providing a better understanding of these techniques in machining control. AI-based methods fall into three categories: knowledge-based expert systems approach, neural networks approach and probabilistic inference approach. In particular, recent research interests mainly tend to develop on-line or real-time expert systems for adapting machining parameters. The use of AI techniques would be valuable for the purpose. (C) 1997 Elsevier Science Limited.
Publisher
ELSEVIER SCI LTD
Issue Date
1998
Language
ENG
Keywords

INFLUENCE DIAGRAMS; OPTIMIZATION

Citation

ARTIFICIAL INTELLIGENCE IN ENGINEERING, v.12, no.1-2, pp.127 - 134

ISSN
0954-1810
DOI
10.1016/S0954-1810(97)00011-3
URI
http://hdl.handle.net/10203/4651
Appears in Collection
KSIM-Journal Papers(저널논문)
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