Prediction of crater morphology and its application for enhancing dimensional accuracy in micro-EDM

Abstract

As a non-conventional machining technique, the micro electrical discharge machining (micro-EDM) process primarily involves the removal of material from the workpiece through high-frequency discharges. The machined surface is covered with multiple overlapping craters to form geometric features with specific surface quality and dimensional accuracy. Consequently, there is a significant need to explore the crater morphology induced by the discharge pulses, which contributes to the precise control of component size and shape. This study targets the identification of material removal in relation to pulse-crater matching within micro-EDM. Initially, pertinent parameters of both pulses and craters are characterized and correlated through a single pulse discharge experiment. Subsequently, accompanied by a pulse classification, a continuous pulse discharge experiment is designed to establish a one-to-one correspondence between erosion craters and the discharge pulses associated with normal discharge, effective discharge, and arc phenomena, which all contribute to material removal. The impact of different discharge pulse types on workpiece material removal is further investigated, with explanations based on energy density and the fraction of energy entering the workpiece. Employing machine learning methods, predictive models for crater-related parameters are developed based on the monitored electrical signals. A comparison of the prediction results from different regression models with various inputs confirms the profound nonlinearity and stochastic nature of the EDM process. Ultimately, the artificial neural network model shows to be optimal in predictive performance, yielding relative errors of 7.81%, 12.49%, and 18.82% for crater diameter, depth, and volume, respectively. Notably, the prediction error for cumulative material removal is only 1.64%, affirming the soundness of the proposed material removal identification for different discharge pulses. Other material removal volume calculation approaches often hinge on machining parameters or statistical distributions. Contrarily, the distinctive characteristic of this approach lies in its implementation of precise pulse-crater correlations of various discharge types based on in-process data. This method is further applied to the prediction of the total material removal volume in micro-EDM drilling. The results are promising for enhancing geometric dimension control in EDM, particularly regarding machining depth.