Improving energy utilization efficiency of electrical discharge milling in titanium alloys machining

Electrical discharge milling(ED-milling) can be a good choice for titanium alloys machining and it was proven that its machining efficiency can be improved to compete with mechanical cutting. In order to improve energy utilization efficiency of ED-milling process, unstable arc discharge and stable arc discharge combined with normal discharge were implemented for material removal by adjusting servo control strategy. The influence of electrode rotating speed and dielectric flushing pressure on machining performance was investigated by experiments. It was found that the rotating of electrode could move the position of discharge plasma channel, and high pressure flushing could wash melted debris out the discharge gap effectively. Both electrode rotating motion and high pressure flushing are contributed to the improvement of machining efficiency.

Optimization of micro milling electrical discharge machining of Inconel 718 by Grey-Taguchi method

The optimization of micro milling electrical discharge machining（EDM） process parameters of Inconel 718 alloy to achieve multiple performance characteristics such as low electrode wear,high material removal rate and low working gap was investigated by the Grey-Taguchi method.The influences of peak current,pulse on-time,pulse off-time and spark gap on electrode wear（EW）,material removal rate（MRR） and working gap（WG） in the micro milling electrical discharge machining of Inconel 718 were analyzed.The experimental results show that the electrode wear decreases from 5.6×10-9 to 5.2×10-9 mm3/min,the material removal rate increases from 0.47×10-8 to 1.68×10-8 mm3/min,and the working gap decreases from 1.27 to 1.19 μm under optimal micro milling electrical discharge machining process parameters.Hence,it is clearly shown that multiple performance characteristics can be improved by using the Grey-Taguchi method.

Electrode Wear Prediction in Milling Electrical Discharge Machining Based on Radial Basis Function Neural Network

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%.