Research On Electrode Wear Prediction And Compensation In Flushing Type Micro-edm Milling

With the development of MEMS(Micro-Electro-Mechanical Systems), there is an increasing demand for fine precision parts and components, and as a kind of micro machining method, micro-EDM(micro-Electrical Discharge Machining) milling has been widely applied. Electrode wear seriously affected the milling precision and machining efficiency in micro-EDM milling, which limited its further development in the field of manufacturing. Therefore, the research on electrode wear prediction and compensation technology in micro-EDM milling is of great significance.The two-dimensional electric field simulation model was established by ANSYS, the influence law of discharge energy, processing depth, electrode morphology and electrode diameter on electric field distribution were studied. And the influence of the electric field distribution of electrode bottom on electrode corner wear was analyzed. Electrode corner wear was studied with experiments under different conditions, thus the interelectrode electric field simulation results were verified. The change rule of surface residual height was studied under different conditions. The residual height of machined surface was influenced by layer thickness, electrode movement speed, discharge energy, electrode diameter and track overlap rate,etc. The influence law of track overlap rate, discharge energy and electrode diameter, layer thickness and electrode speed on processing surface residual height were studied.On electrode axial wear, the single factor experiments about discharge energy, processing depth, electrode diameter, layer thickness, milling direction and speed of electrode movement on electrode axial wear were conducted, the influence of these factors on electrode axial wear were analyzed. The primary and secondary impact order of these factors to electrode axial wear was obtained by orthogonal experiment, which found that discharge energy was the biggest factor affecting electrode axial wear in these six factors and the direction of the milling is the second.Based on the contour of single channel slot bottom, the electrode axial wear model was established. And the parameters in electrode axial wear model were forecasted through the generalized regression neural network. The electrode axial wear prediction model on 6 sets parameters were obtained simultaneously by GRNN, which the root-mean-square error is only 0.21μm and there were 5 sets of parameters’ prediction error within 10%. Based on electrode axial wear prediction model and the correction of fixed length compensation method, two methods that each compensation dosage is non fixed-length under each corresponding processing length is fixed and each compensation dosage is fixed-length under each corresponding processing length is non fixed to compensating electrode axial wear were put forward. Some single channel grooves and cavities as experimental verification were machined under different processing parameters. By comparing these cavities with no compensation, compensated respectively by fixed-length compensation and these two compensations which proposed in this paper, it obviously found that the cavity bottom shape accuracy is greatly improved by using the newly proposed electrode wear compensation methods.