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Abstract

Digital twin technology has emerged as an effective approach for improving the monitoring, validation, and optimization of Computer Numerical Control (CNC) machining operations. This study presents an investigative framework integrating Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM), Software-in-the-Loop (SIL), and Hardware-in-the-Loop (HIL) methodologies for diagnosing and rectifying machining defects in CNC systems using the digital twin - virtual model (component and asset twins). Four machining experiments were conducted using Aluminum 6061-T6, Polytetrafluoroethylene (PTFE), Brass, and Stainless Steel (AISI 304) on an EMCO CNC machine integrated with Siemens 840D controls. The first two experiments, involving turning and drilling operations, are presented in detail; the third and fourth experiments, conducted on Brass and Stainless Steel (AISI 304), are included as additional experimental validations under different machining conditions to evaluate the robustness and repeatability of the proposed methodology. Real-time machining parameters, including spindle speed, feed rate, axis offsets, and drilling depth, were synchronized between the virtual simulation environment and the physical CNC machine through digital twin-based validation. The proposed framework successfully identified dimensional deviations, tool offset errors, and drill alignment defects before and during machining operations. Experimental validation demonstrated a reduction in dimensional error from approximately +0.50 mm to within ± 0.05 mm tolerance limits after digital twin optimization. Furthermore, optimization of contour-turning strategy reduced the CAM simulation time from 5.0 min to 3.0 min, corresponding to approximately a 40% improvement in process efficiency. Repeatability tests conducted over three machining trials confirmed consistent machining accuracy and process stability. The results demonstrate that digital twin-assisted CNC machining can significantly improve dimensional precision, reduce machining errors, minimize setup time, and enhance production reliability in smart manufacturing environments.

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