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CLC number: TG5; TP2

On-line Access: 2007-11-10

Received: 2007-03-12

Revision Accepted: 2007-07-05

Crosschecked: 0000-00-00

Cited: 23

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.1 P.104~108

http://doi.org/10.1631/jzus.A071242


Parameter optimization model in electrical discharge machining process


Author(s):  Qing GAO, Qin-he ZHANG, Shu-peng SU, Jian-hua ZHANG

Affiliation(s):  School of Mechanical Engineering, Shandong University, Jinan 250061, China

Corresponding email(s):   gaoqing@mail.sdu.edu.cn, zhangqh@sdu.edu.cn

Key Words:  Electrical discharge machining (EDM), Genetic algorithm (GA), Artificial neural network (ANN), Levenberg-Marquardt algorithm


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",
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pages="104~108",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A071242"
}

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%T Parameter optimization model in electrical discharge machining process
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%A Qin-he ZHANG
%A Shu-peng SU
%A Jian-hua ZHANG
%J Journal of Zhejiang University SCIENCE A
%V 9
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%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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[2] Das, S., Klotz, M., Klocke, F., 2003. EDM simulation: finite element-based calculation of deformation, microstructure and residual stresses. Journal of Materials Processing Technology, 142(2):434-451.

[3] Ho, K.H., Newman, S.T., 2003. State of the art electrical discharge machining (EDM). International Journal of Machine Tools and Manufacture, 43(13):1287-1300.

[4] Kuriakose, S., Shunmugam, M.S., 2005. Multi-objective optimization of wire-electro discharge machining process by non-dominated sorting genetic algorithm. Journal of Materials Processing Technology, 170(1-2):133-141.

[5] Li, X.L., Yin, G.F., Lin, C.Y., 2004. Tool wear prediction in electrical discharge milling machining based on evolutionary neural network. Chinese Journal of Mechanical Engineering, 40(3):61-64 (in Chinese).

[6] Mandal, D., Pal, S.K., Saha, P., 2007. Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II. Journal of Materials Processing Technology, 186(1-3):154-162.

[7] Rao, S.S., 1991. Optimization Theory and Applications. Wiley Eastern Limited, New Delhi.

[8] Tsai, K.M., Wang, P.J., 2001. Predictions on surface finish in electrical discharge machining based upon neural network models. International Journal of Machine Tools and Manufacture, 41:1385-1403.

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[10] Yang, X.D., Zhao, W.S., 2005. The artificial neural network technology for EDM process forecasting based on web. Journal of Harbin Institute of Technology, 37(8):1029-1031 (in Chinese).

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