基于BP神经元网络优化电火花加工参数  

Optimization of electric discharge machining parameters based on BP neural network

作　　者：孙悦[1] 易鹏飞[1] 

机构地区：[1]四川大学高温高压物理所,四川成都610065

出　　处：《超硬材料工程》2008年第6期23-27,共5页Superhard Material Engineering

基　　金：国家自然科学基金和中物院基金联合资助项目(No.10576020)

摘　　要：基于BP神经网络模型对用电极(铜Cu)-材料(模具钢NAK 80)电火花加工系统的一组参数进行了优化。计算结果表明,减少输入层神经元的数量可以加快收敛速度,但计算精度将受到影响。若保持隐层结构,仅由不同训练函数的学习结果改变权系数(映射)实现网络结构优化,则可能较好地预言加工效果。Based on BP neural network, a group of parameters of electric discharge machining system by using electrode （Cu）-material （die steel NAK80） has been optimized. The calculating results has shown decreasing the amount of neuron in inputting layer can accelerate convergence with some affection on calculating precision. But if keeping the structure of hidden layer, a better prediction of processing effects would be attained by optimizing network structure only through changing weight coefficient （mapping） according to the studying results of different training function.

关 键 词：BP神经元网络 电火花加工 超硬材料 

分 类 号：TG661[金属学及工艺—金属切削加工及机床]