REAL-TIME MACHINING QUALITY CONTROL THROUGH DIGITAL TWIN AND PREDICTIVE ANALYTICS

Abstract

Real-time machining quality control has become essential for modern manufacturing because product quality, dimensional accuracy, and production efficiency depend on continuous monitoring of machining conditions. Conventional quality inspection techniques are primarily performed after machining, resulting in delayed defect detection, increased production cost, and material wastage. This paper proposes a real-time machining quality control framework integrating Digital Twin technology, predictive analytics, Industrial Internet of Things (IIoT), machine learning, intelligent sensors, and cloud manufacturing for continuous monitoring and adaptive optimization of machining processes. The proposed methodology synchronizes physical machining operations with virtual Digital Twin models capable of predicting machining quality and recommending corrective actions before defects occur. Experimental evaluation demonstrates improvements in surface quality, dimensional accuracy, machining stability, tool utilization, production efficiency, predictive maintenance, and operational reliability. The proposed framework provides an intelligent, scalable, and Industry 4.0-ready solution for real-time machining quality control and sustainable manufacturing. Keywords— Digital Twin, Predictive Analytics, Machining Quality Control, Intelligent Manufacturing, Machine Learning, Industrial Internet of Things, Smart Manufacturing, Adaptive Process Control.