The Methods And Programming Of Electrical Discharging States In Micro Electrical Machining

As an important branch of micro-machining technology, micro-EDM has found its wide applications in industry for its untouched machining process, machining surface with high aspect ratio easily and high capability in machining micro 3D structure. Compared with normal EDM, in micro-EDM the electrical power used has to be small for micro machining, say 10^-7-10^-6J, and the frequency of discharging pulses has to be high, as high as up to 1 Mhz or even higher. Thus, the gap state distinguishing method with higher reliability and the micro-energy pulse generator are proposed in micro-EDM's key technologiesHigh frequency and low electrical power of micro EDM causes the waveforms of voltage and current highly distorted, thus indistinguishable by the conventional EDM discrimination systems due to the low ratio of signal to noise. Such uncertainty of signals in discrimination of discharging states really brought a new challenge in micro EDM field. From the observation of the signals of voltage and current, we found that the information contained in current is complementary to the information in voltage for discriminating the different discharging states. Thus a new intelligent discrimination system was established. This intelligent system consists of three modes, a fuzzy discrimination mode, a LVQ neural network mode and a fuzzy generalization mode. To solve the uncertainty in the sampled signals from the gap, the signals of voltage and current are taken simultaneously as inputs of the fuzzy discrimination mode. The inputs to fuzzy discrimination mode are then treated by the established fuzzy inference mechanism and the discharging state of the sampled signal is thus denoted by a value in one of the four intervals, each representing separately one of the four discharging states, namely short state, off state, spark/arc state and open state. The value then is fed into the LVQ neural network mode and converted into one of four vectors of four by one, each of which represents a different discharging state by defining the corresponding element being one and the other three being zeros in the vector. After the signals from a group of pulses are serially treated in the same way, summed one by one and finally divided by the number of the sampled signals, the elements in the vector represent the ratios of four discharging states. The ratios of short state and spark/arc state are then put into the final fuzzy generalization mode and the discharging state in the gap can be statistically generalized accordingly.