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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2008, Vol. 9 Issue (1): 104-108    DOI: 10.1631/jzus.A071242
Civil and Mechanical Engineering     
Parameter optimization model in electrical discharge machining process
Qing GAO, Qin-he ZHANG, Shu-peng SU, Jian-hua ZHANG
School of Mechanical Engineering, Shandong University, Jinan 250061, China
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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.

Key words： Electrical discharge machining (EDM)      Genetic algorithm (GA)      Artificial neural network (ANN)      Levenberg-Marquardt algorithm     
Received: 12 March 2007      Published: 10 November 2007
CLC:  TG5  
  TP2  
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Qing GAO
Qin-he ZHANG
Shu-peng SU
Jian-hua ZHANG
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Qing GAO, Qin-he ZHANG, Shu-peng SU, Jian-hua ZHANG. Parameter optimization model in electrical discharge machining process. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2008, 9(1): 104-108.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.A071242     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2008/V9/I1/104

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