A Digital Twin Trinity for Adaptive Evaluation of Machining Process

Digital Twins (DT) are crucial in cyber-physical manufacturing systems. However, achieving accuracy, real-time processing and precision in machining applications remains a significant challenge. This work introduces the Digital Twin Trinity (DTT) framework to address these challenges, integrating physical simulation, surrogate inference, and metrology certification for CNC machining. The physical simulation stage captures fundamental machining physics, including cutting force effects and thermal influences based on process parameters. A recurrent-based neural network is a surrogate inference model, enabling real-time predictions of cutting forces and tool wear. This enhances the accuracy of digital twin simulations. Finally, a Smart Tool Holder (STH) acts as a metrology certification tool, validating the physical simulation and ensuring precision in machining operations.