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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%. 相似文献
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