Investigation of the Effect of Tool Rotation Rate in EDM Drilling of Ultrafine Grain Tungsten Carbide Using Predictive Machine Learning

Electric discharge machining (EDM) is widely employed for machining hard, conductive materials. Tool rotation has emerged as an effective strategy to enhance debris flushing and improve stability during deep-hole EDM drilling. This study proposes a machine learning-based approach to evaluate the influence of tool rotation and predict the unstable machining conditions in EDM of ultrafine grained tungsten carbide. A structured analytical workflow, combining Taguchi–Grey optimization, regression analysis, and classification models, was designed to capture discharge dynamics across macro- and micro-timescales. Classification models trained on raw and processed electrical signal features achieved over 88% accuracy and 90% recall. SHAP analysis revealed that the relationship between key discharge events such as sparks and short circuits varied significantly across stable and unstable machining phases, underscoring the importance of phase-specific modeling. While correlation analysis showed weak global associations, phase-dependent SHAP values revealed opposing feature effects, allowing the context-informed interpretation of model behavior. Phase segmentation revealed that, compared to 1000 RPM, short circuits were reduced by about 40% during stable machining at 8000–9000 RPM. Conversely, during unstable phases, spark effectiveness dropped by nearly 45%, and secondary discharges increased throughout this range. These insights support the design of adaptive control strategies that adjust the rotation rate in response to detected phase changes, aiming to sustain machining stability. The findings support the development of dynamic control frameworks to improve EDM performance, particularly for mold fabrication using tungsten carbide.