Development of Parameters Selection System for Ultrasonic Vibration Assisted Electro-Discharge Machining

Because the machining of ultrasonic vibration assisted electro-discharge machining (UEDM) is a very complex process and it is too difficult to describe precisely every influencing factor with an accurate mathematics model, the study of parameters selection system is necessary and important for the practical application of machining method, the improvement of machining efficiency and minimizing the tool wear ratio (TWR). In this paper, the model and the corresponding database are built for UEDM based on the back propagation (BP) algorithm artificial neural network (ANN) to optimize machining parameters. Through learning and training, this system realizes the intelligent selection of machining parameters. As shown by the experiment results, the predictions accord with the test results, which shows that the reasonable and reliable project of UEDM can be provided by the system. With the increase of the machining sample, the machining database can be enriched and the application range will be expanded, so this system has the excellent fault-tolerance and extensible quality.