DIGITAL TWIN-BASED SMART MANUFACTURING FRAMEWORK FOR REAL-TIME PROCESS OPTIMIZATION

Abstract

Smart manufacturing has emerged as a key component of Industry 4.0 by integrating cyberphysical systems, artificial intelligence, Industrial Internet of Things (IIoT), and advanced analytics to improve production efficiency and operational intelligence. However, traditional manufacturing systems face challenges related to real-time monitoring, process optimization, predictive maintenance, and adaptive decision-making. This paper proposes a Digital Twin-based smart manufacturing framework for real-time process optimization by integrating Digital Twin technology, machine learning, IIoT-enabled sensing, cloud computing, predictive analytics, and intelligent control mechanisms. The proposed framework establishes a continuous connection between physical manufacturing systems and virtual models to monitor operations, predict performance variations, optimize process parameters, and support autonomous decisionmaking. Experimental evaluation demonstrates improvements in process efficiency, product quality, predictive accuracy, resource utilization, equipment performance, and production reliability. The proposed framework provides a scalable solution for intelligent manufacturing transformation and enables sustainable, adaptive, and data-driven industrial operations. Keywords— Digital Twin, Smart Manufacturing, Industry 4.0, Real-Time Optimization, IIoT, Machine Learning, Predictive Analytics, CyberPhysical Systems.