Digital Twin Networks: Simulation-Driven Optimization for Industry 4.0
DOI:
https://doi.org/10.15662/IJARCST.2025.0802002Keywords:
Digital Twin, Digital Twin Network, Industry 4.0, simulation, optimization, metaheuristic, reinforcement learning, IIoT, production systems, simulation-driven designAbstract
Digital Twin (DT) technology has emerged as a transformative force in Industry 4.0, enabling real-time digital replicas of physical systems that support monitoring, simulation, and optimization. This paper explores Digital Twin Networks (DTNs)—virtual models of networked industrial environments—designed to drive performance enhancements and resilience through simulation-driven optimization. Core contributions include an overview of DTN concepts, modeling techniques, and optimization methodologies; a proposed framework integrating DTNs with machine learning and metaheuristic optimization; and a demonstration through case studies such as PCB drilling and digital production system design.
DTNs facilitate bidirectional synchronization between physical systems and their virtual counterparts, enabling realtime data exchange for predictive maintenance, resource planning, and scenario testing. Optimization embedded in DTNs—via metaheuristic algorithms or reinforcement learning—enhances throughput, reduces downtime, and supports adaptive control. Case studies reveal that DT-integrated optimization can triple manufacturing throughput in PCB drilling and effectively support simulation-based design of production systems. Additionally, DTNs empower stochastic computation offloading in IIoT environments, improving energy efficiency under uncertainty.
The paper outlines the architecture, workflow, strengths, and limitations of simulation-driven DTNs. Advantages include high-fidelity modeling, proactive decision-making, and flexible experimentation. Challenges involve data integration complexity, security vulnerabilities, modeling fidelity, and the absence of universal frameworks. Future work will focus on standardization, explainable AI integration, uncertainty quantification, and expansion toward fully integrated DTNs for networked industrial systems.
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