CLC number: TG5; TP2
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 23
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Qing GAO, Qin-he ZHANG, Shu-peng SU, Jian-hua ZHANG. Parameter optimization model in electrical discharge machining process[J]. Journal of Zhejiang University Science A, 2008, 9(1): 104-108.
@article{title="Parameter optimization model in electrical discharge machining process",
author="Qing GAO, Qin-he ZHANG, Shu-peng SU, Jian-hua ZHANG",
journal="Journal of Zhejiang University Science A",
volume="9",
number="1",
pages="104-108",
year="2008",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A071242"
}
%0 Journal Article
%T Parameter optimization model in electrical discharge machining process
%A Qing GAO
%A Qin-he ZHANG
%A Shu-peng SU
%A Jian-hua ZHANG
%J Journal of Zhejiang University SCIENCE A
%V 9
%N 1
%P 104-108
%@ 1673-565X
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A071242
TY - JOUR
T1 - Parameter optimization model in electrical discharge machining process
A1 - Qing GAO
A1 - Qin-he ZHANG
A1 - Shu-peng SU
A1 - Jian-hua ZHANG
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 1
SP - 104
EP - 108
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A071242
Abstract: electrical discharge machining (EDM) process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper, artificial neural network (ANN) and genetic algorithm (GA) are used together to establish the parameter optimization model. An ANN model which adapts levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate (MRR) and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.
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