Hybrid Digital Twin Architectures for Real–Time Decision Making in Industry 4.0    

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ABSTRACT

The emergence of Hybrid Digital Twin (HDT) architectures is revolutionizing real-time decision-making in Industry 4.0, enabling intelligent automation, predictive maintenance, and optimized production workflows. This research introduces a multi-layered HDT framework that integrates physics-based modeling, AI-driven analytics, and edge-cloud computing to enhance industrial system responsiveness and resilience. The proposed architecture employs reinforcement learning-based adaptive control, federated digital twins, and blockchain-enhanced security to ensure seamless synchronization between virtual and physical assets while maintaining data integrity. Experimental validation across smart manufacturing, energy grids, and industrial robotics demonstrates significant improvements over conventional digital twin models, including a 30% reduction in system downtime, a 45% improvement in predictive accuracy, and a 25% enhancement in operational efficiency. The HDT system facilitates real-time cyber-physical convergence, allowing industries to dynamically adapt to changing operational conditions and optimize decision-making in complex environments. Additionally, the federated learning approach ensures privacy-preserving collaboration among distributed digital twins, while blockchain integration enhances security and trust in data transactions. The study highlights the scalability, robustness, and real-time adaptability of the proposed HDT framework, making it a viable solution for smart factories, healthcare systems, and industrial IoT applications. Future research directions include optimizing federated aggregation techniques, reducing computational overhead in privacy-preserving mechanisms, and integrating edge computing for faster decision-making. This work contributes to the advancement of intelligent cyber-physical systems by providing a secure, scalable, and adaptive digital twin architecture for Industry 4.0.

Significance of the Study:

This study introduces a Hybrid Digital Twin (HDT) architecture that revolutionizes Industry 4.0 through real-time decision-making, predictive maintenance, and optimized workflows. By integrating physics-based modeling, AI analytics, and edge-cloud computing, the framework achieves 30% lower downtime, 45% higher predictive accuracy, and 25% improved efficiency. Federated learning ensures privacy-preserving collaboration, while blockchain enhances data security. The HDT’s adaptive control and cyber-physical synchronization make it ideal for smart factories, energy grids, and industrial IoT, advancing intelligent automation in complex environments.

Summary of the Study:

The study proposes a multi-layered Hybrid Digital Twin (HDT) framework for Industry 4.0, combining reinforcement learning, federated digital twins, and blockchain security. Validated in manufacturing, energy, and robotics, it reduces downtime by 30%, boosts predictive accuracy by 45%, and enhances efficiency by 25%. The architecture enables real-time cyber-physical synchronization with privacy-preserving federated learning and secure blockchain transactions. Future work focuses on optimizing federated aggregation and edge computing integration, positioning HDT as a scalable, adaptive solution for smart industrial systems.