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

Abstract

Ultrasonic vibration-assisted electrical discharge machining (UV-EDM) requires careful tuning of multiple interacting 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. The framework enables adaptive, uncertainty-aware process monitoring and optimization in near real-time. Validation on 150 experimental UV-EDM trials demonstrates: (1) predictive R² = 0.92 for MRR and R² = 0.89 for Ra, (2) 14.6Beyond numerical performance, the proposed system highlights the value of hybrid intelligence — combining data-driven learning with physical domain understanding — to achieve consistent 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, making it adaptable for both research and industrial deployment. Overall, this work establishes a tangible pathway toward smart manufacturing, bridging the gap between physical machining and its virtual counterpart through AI and IoT integration.

Cite as :

Sunayana P Singh, Tameem Ulla, Tanvi Muttin, Vagarth Pandey, & Gajanan M Naik. (2026). Digital Twin Framework for Real-Time Monitoring and Optimization of Ultrasonic Vibration-Assisted EDM using AI and IoT. Research and Reviews on Experimental and Applied Mechanics, 9(1), 1–10. https://doi.org/10.5281/zenodo.18126988