期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Electrode Wear Prediction in Milling Electrical Discharge Machining Based on Radial Basis Function Neural Network 被引量:2
1
作者 黄河 白基成 +1 位作者 卢泽生 郭永丰 《Journal of Shanghai Jiaotong university(Science)》 EI 2009年第6期736-741,共6页
Milling electrical discharge machining(EDM) enables the machining of complex cavities using cylindrical or tubular electrodes.To ensure acceptable machining accuracy the process requires some methods of compensating f... Milling electrical discharge machining(EDM) enables the machining of complex cavities using cylindrical or tubular electrodes.To ensure acceptable machining accuracy the process requires some methods of compensating for electrode wear.Due to the complexity and random nature of the process,existing methods of compensating for such wear usually involve off-line prediction.This paper discusses an innovative model of electrode wear prediction for milling EDM based upon a radial basis function(RBF) network.Data gained from an orthogonal experiment were used to provide training samples for the RBF network.The model established was used to forecast the electrode wear,making it possible to calculate the real-time tool wear in the milling EDM process and,to lay the foundations for dynamic compensation of the electrode wear on-line.This paper demonstrates that by using this model prediction errors can be controlled within 8%. 展开更多
关键词 milling electrical discharge machining edm electrode wear prediction radial basis function (RBF) neural network
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部