A machine learning-based classification model to identify the effectiveness of vibration for µEDM

Abstract

Micro electro-discharge machining (µEDM) uses electro-thermal energy from repetitive sparks generated between the tool and workpiece to remove material from the latter. However, one of the bottlenecks of µEDM is the phenomenon of short circuits due to the physical contact between the tool and debris (formed during the erosion of the workpiece). Adequate flushing of the debris can be achieved by applying low amplitude high-frequency vibration to the workpiece. This study, however, shows that the application of vibration does not yield beneficial results for the µEDM for all the parametric conditions. This research used an off-the-shelf piezo vibrator as the high-frequency, low amplitude vibration source to the workpiece during the µEDM process. The experiments were conducted with and without vibration with the variation of applied discharge energy and µEDM speed. The samples were characterized using scanning electron microscopes to gather various data related to µEDM outputs. The results of this study revealed that vibration-assisted µEDM becomes less effective as the discharge energy is increased (primarily by increasing the capacitor value of the RC pulse generator). Similarly, the reduction of the occurrence of the short circuit was profound when the low discharge energy level with low voltage and low capacitor setting of the RC Pulse generator was used. The overall scale of the overcut with various discharge energy and µEDM speed varied from 15.5 µm to 42 µm for the conventional µEDM process. However, the scale above slightly reduced to 14.5 µm to 39 µm using an ultrasonic vibration device. Also, the taperness of the machined hole was slightly reduced by applying the vibration device during the µEDM operation (overall average of ~7%).