DIGITAL TWIN-ASSISTED MACHINING PARAMETER OPTIMIZATION FOR REAL-TIME QUALITY IMPROVEMENT

Abstract

Digital Twin technology has emerged as a transformative approach for intelligent manufacturing by enabling real-time synchronization between physical machining systems and virtual process models. Conventional machining parameter optimization often relies on offline experimentation, limiting the ability to respond to changing machining conditions during production. This paper proposes a Digital Twin-assisted machining parameter optimization framework integrating Industrial Internet of Things (IIoT), machine learning, predictive analytics, cloud manufacturing, intelligent sensors, and real-time process monitoring for continuous quality improvement in turning operations. The proposed methodology establishes a virtual representation of the machining environment that continuously analyzes sensor data, predicts machining quality, and recommends adaptive parameter optimization strategies. Experimental evaluation demonstrates improvements in surface quality, dimensional accuracy, tool utilization, machining stability, production efficiency, and predictive maintenance performance. The proposed framework provides an intelligent, scalable, and Industry 4.0-ready solution for real-time machining parameter optimization and manufacturing quality enhancement.