DIGITAL TWIN-ENABLED QUALITY ASSURANCE FOR REAL-TIME METAL ADDITIVE MANUFACTURING

Abstract

Digital Twin technology has become a transformative solution for intelligent manufacturing by enabling real-time synchronization between physical production systems and virtual process models. In metal additive manufacturing, Digital Twins facilitate continuous quality monitoring, predictive analytics, defect detection, and adaptive process optimization, thereby improving production efficiency and component reliability. This paper proposes a Digital Twin-enabled quality assurance framework integrating Industrial Internet of Things (IIoT), machine learning, multi-sensor data fusion, cloud computing, predictive analytics, adaptive process control, and manufacturing execution systems. The proposed methodology continuously monitors manufacturing operations, predicts quality deviations, optimizes process parameters, and supports automated quality assurance throughout the production lifecycle. Experimental evaluation demonstrates improvements in defect detection accuracy, dimensional precision, production stability, equipment utilization, material efficiency, and manufacturing productivity. The proposed framework provides a scalable and intelligent solution for Industry 4.0-enabled metal additive manufacturing while supporting sustainable production, real-time quality assurance, and autonomous manufacturing optimization.