Generation and evolutionary learning of cutting conditions for milling operations

Authors
Park, BTPark, MWKim, SK
Issue Date
2001-12
Publisher
SPRINGER-VERLAG LONDON LTD
Citation
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, v.17, no.12, pp.870 - 880
Abstract
In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper presents a new methodology for continual improvement of cutting conditions. It is called GELCC (generation and evolutionary learning of cutting conditions). GELCC is a key component of an operation planning system for milling operations. It performs the following three functions. 1. The modification of recommended cutting conditions obtained from a machining data handbook. 2. The incremental learning of obtained cutting conditions using fuzzy ARTMAP neural networks. 3. The substitution of better cutting conditions for those learned previous by a proposed replacement algorithm. Various simulations illustrate the performance of GELCC. and then the simulation results for a given part are provided and discussed.
Keywords
OPTIMIZATION; SYSTEM; ART; OPTIMIZATION; SYSTEM; ART; computer-aided process planning (CAPP); cutting conditions; neural network; operation planning
ISSN
0268-3768
URI
https://pubs.kist.re.kr/handle/201004/139989
DOI
10.1007/s001700170098
Appears in Collections:
KIST Article > 2001
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