Parameter optimization model in electrical discharge machining process
Parameter optimization model in electrical discharge machining process
复制标题
放电加工过程参数优化模型
DOI：
10.1631/jzus.a071242
复制
发表时间：
2008-01-01
影响因子：
3.2
通讯作者：
Zhang, Jian-hua
中科院分区：
文献类型：
--
作者：
Gao, Qing;Zhang, Qin-he;Zhang, Jian-hua
电火花加工工艺，目前仍是一种经验加工工艺，其所选参数往往与最优参数相差甚远，同时选择最优参数成本高、耗时长。本文将人工神经网络（ANN）和遗传算法（GA）相结合，建立了参数优化模型。采用Levenberg-Marquardt算法建立了材料去除率与输入参数之间的神经网络模型，并采用遗传算法对模型参数进行优化，得到优化结果。结果表明，该模型是有效的，并通过优化加工参数提高了MRR。
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.