Parameter optimization model in electrical discharge machining process

被引：38

作者：

Gao, Qing ^{[1
]}

Zhang, Qin-he ^{[1
]}

Su, Shu-peng ^{[1
]}

Zhang, Jian-hua ^{[1
]}

机构：

[1] Shandong Univ, Sch Mech Engn, Jinan 250061, Peoples R China

来源：

JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A | 2008年 / 9卷 / 01期

基金：

中国国家自然科学基金;

关键词：

electrical discharge machining (EDM); genetic algorithm (GA); artificial neural network (ANN); Levenberg-Marquardt algorithm;

D O I：

10.1631/jzus.A071242

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：104 / 108

页数：5

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