As advanced materials with high hardness, strength, and heat resistance are increasingly applied in fields such as aerospace, semiconductors, biomedical engineering, and mold manufacturing, the demand for high-precision machining technologies is growing. Micro electrical discharge machining (Micro-EDM) has gained attention as a non-contact process that locally melts and vaporizes conductive materials using electrical sparks, allowing for the fabrication of intricate 3D microstructures with high precision. This study analyzes the impact of capacitance in RC-type discharge circuits on the machining characteristics of single discharge craters using aluminum, brass, copper, STS304, and WC-Co. Additionally, we compare the overlapping behavior and morphological evolution of multiple discharge craters across these materials. We investigated the diameter and depth of single discharge craters, as well as the geometrical characteristics of overlapped craters. The results demonstrate the influence of discharge energy and material properties on discharge crater geometry, providing a quantitative basis for analyzing surface morphology in the Micro-EDM process.

CNN is one of the deep learning technologies useful for image-based pattern recognition and classification. For machining processes, this technique can be used to predict machining parameters and surface roughness. In electrical discharge machining (EDM), the machined surface is covered with many craters, the shape of which depends on the workpiece material and pulse parameters. In this study, CNN was applied to predict EDM parameters including capacitor, workpiece material, and surface roughness. After machining three metals (brass, stainless steel, and cemented carbide) with different discharge energies, images of machined surfaces were collected using a scanning electron microscope (SEM) and a digital microscope. Surface roughness of each surface was then measured. The CNN model was used to predict machining parameters and surface roughness.