Glassy carbon (GC) has superior properties such as high corrosion resistance, heat resistance, and low adhesion to glass materials in a glass molding process (GMP). In addition, the demand for GC molds is increasing in various industries that require high precision of glass parts. However, GC is a difficult-to-machine material with high hardness and brittleness. Electrical discharge machining (EDM) can machine GC regardless of its strength or hardness. In this study, tungsten carbide (WC-Co) electrode was fabricated by wire electrical discharge grinding (WEDG). Characteristics of EDM of micro holes on GC were then analyzed. As capacitance and voltage increased, material removal rate (MRR) increased while machining time tended to decrease. However, at low voltages, short circuit and secondary discharge occurred, which increased the electrode wear rate (EWR). As a result, a D-shaped electrode that could prevent short circuit and debris accumulation was fabricated and a micro hole array was machined.

As the digitization of the manufacturing process is accelerating, various data-driven approaches using machine learning are being developed in chemical mechanical polishing (CMP). For a more accurate prediction in contact-based CMP, it is necessary to consider the real-time changing pad surface roughness during polishing. Changes in pad surface roughness result in non-uniformity of the real contact pressure and friction applied to the wafer, which are the main causes of material removal rate variation. In this paper, we predicted the material removal rate based on pressure and surface roughness using a deep neural network (DNN). Reduced peak height (Rpk) and real contact area (RCA) were chosen as the key parameters indicative of the surface roughness of the pad, and 220 data were collected along with the process pressure. The collected data were normalized and separated in a 3 : 1 : 1 ratio to improve the predictive performance of the DNN model. The hyperparameters of the DNN model were optimized through random search techniques and 5 cross-validations. The optimized DNN model predicted the material removal rate with high accuracy in ex-situ CMP. This study is expected to be utilized in data-driven machine learning decision making for cyber-physical CMP systems in the future.