CYBER-PHYSICAL DIGITAL TWIN ARCHITECTURE FOR ADAPTIVE PRODUCTION CONTROL IN INTELLIGENT FACTORIES

Abstract

The rapid evolution of intelligent factories has created a strong requirement for production control architectures capable of responding dynamically to machine degradation, process disturbances, quality variation, changing workloads, material inconsistency, energy constraints, and unexpected equipment events. Conventional production control systems frequently depend on static schedules, isolated automation logic, predefined thresholds, and fragmented information flows that provide limited adaptability under continuously changing industrial conditions. Cyber-physical systems and digital twin technologies provide an opportunity to establish persistent interaction between physical manufacturing assets and their virtual representations, enabling real-time monitoring, predictive assessment, simulationassisted decision-making, and adaptive production coordination. This paper proposes a cyber-physical digital twin architecture for adaptive production control in intelligent factories. The proposed methodology integrates heterogeneous industrial assets, Industrial Internet of Things sensors, programmable controllers, edge gateways, secure communication services, standardized APIs, synchronized digital twin models, machine learning analytics, predictive maintenance intelligence, adaptive production decision mechanisms, and governed control feedback. The architecture continuously acquires vibration, temperature, machine state, tool condition, production rate, quality indicators, energy consumption, and operational context from physical manufacturing systems. Edge services validate and preprocess incoming observations before synchronizing corresponding digital twin states. Analytical services identify degradation patterns, predict equipment risk, evaluate process performance, and assess alternative production responses. An adaptive decision layer combines digital twin state, asset criticality, production priorities, machine availability, quality risk, and operational constraints before generating recommendations or authorized supervisory control actions. Secure API lifecycle management protects service communication, while production-oriented model operations support controlled deployment and monitoring of analytical models. Representative experimental analysis indicates that the proposed architecture can reduce production response latency, decrease unplanned downtime, improve throughput, increase schedule adherence, reduce defect rates, and strengthen digital twin synchronization compared with conventional static production control. The proposed architecture provides a scalable foundation for resilient, adaptive, secure, and intelligence-driven production management in next-generation smart factories.