INTEGRATED DIGITAL TWIN AND IOT FRAMEWORK FOR REALTIME MONITORING OF ADVANCED MANUFACTURING PROCESSES

Abstract

The rapid transformation of conventional production environments into intelligent and interconnected manufacturing ecosystems has created an increasing demand for continuous process visibility, adaptive monitoring, operational intelligence, and timely decisionmaking. Advanced manufacturing processes involve complex interactions among machines, production parameters, materials, sensing devices, control systems, and enterprise applications, making conventional periodic inspection and isolated monitoring approaches insufficient for achieving high levels of productivity and reliability. This paper proposes an integrated Digital Twin and Internet of Things framework for real-time monitoring of advanced manufacturing processes. The proposed framework combines Industrial Internet of Things sensing, edge-enabled data acquisition, heterogeneous data preprocessing, multi-source information fusion, dynamic Digital Twin synchronization, intelligent process analytics, anomaly identification, condition assessment, visualization, and feedback-driven decision support. Physical manufacturing assets are continuously observed through vibration, temperature, acoustic, pressure, electrical current, rotational speed, tool condition, energy consumption, and environmental sensors. The acquired information is processed and transformed into synchronized virtual representations capable of reflecting the current operational condition of machines and production processes. The Digital Twin layer maintains contextual information regarding machine configuration, operating state, historical behavior, production parameters, quality indicators, and maintenance events. An intelligent analytics layer evaluates real-time process deviations, abnormal operating patterns, equipment deterioration, quality risks, and production inefficiencies. The framework further employs interoperable application programming interfaces to connect heterogeneous sensing platforms, virtual models, analytical services, dashboards, and industrial applications. Representative experimental evaluation indicates that the integrated approach improves monitoring accuracy, reduces abnormal event detection delay, decreases false alarms, enhances process visibility, and supports earlier identification of manufacturing deviations compared with conventional threshold-based monitoring. The framework also provides scalable deployment through coordinated edge and cloud computing resources. The results demonstrate that integrating Digital Twin technology with IoT-enabled sensing creates a dynamic cyber-physical monitoring environment capable of supporting resilient, efficient, sustainable, and intelligent manufacturing operations.