Digital Twin Framework for Real-Time Monitoring and Optimization of Ultrasonic Vibration-Assisted EDM using AI and IoT

Ultrasonic vibration-assisted electrical discharge machining (UV-EDM) requires careful tuning of multiple in teracting parameters to balance Material Removal Rate (MRR) and surface roughness (Ra). This paper presents an integrated Digital Twin framework coupling real-time IoT sensor streams with Gaussian Process Regression (GPR) surrogate modeling, NSGA-II multi-objective optimization, and Analytic Hierarchy Process (AHP) decision support. Validation on 150 experimental UV-EDM trials demonstrates: (1) predictive R2 = 0.92 for MRR and R2 = 0.89 for Ra, (2) a 14.6% improvement in MRR, and (3) a 9.2% reduction in Ra compared to baseline settings. Beyond numerical performance, the proposed system high lights the value of hybrid intelligence by combining data-driven learning with physical domain understanding to achieve con sistent machining quality under varying operational conditions. The digital twin’s continuous feedback loop allows operators to visualize tool wear, spark energy, and machining stability in real time, promoting proactive decision-making rather than reactive control. Furthermore, the framework’s modular design ensures scalability across different EDM configurations, mak ing it adaptable for both research and industrial deployment. Overall, this work establishes a practical pathway toward smart manufacturing by bridging the gap between physical machining and its virtual counterpart through AI and IoT integration.