Digital Twin Frameworks for Predictive Maintenance and Autonomous Decision-Making in Industry 5.0

Abstract

The transition from Industry 4.0 to Industry 5.0 emphasizes human-centric, resilient, and sustainable manufacturing systems where intelligent machines collaborate with people rather than merely automating tasks. Digital twin frameworks have emerged as a foundational technology for this transformation by creating dynamic virtual replicas of physical assets, processes, and production environments. These virtual models continuously synchronize with real-world data to support predictive maintenance, optimization, simulation, and autonomous decision-making. This paper investigates digital twin frameworks for predictive maintenance and autonomous decision-making in Industry 5.0. It examines enabling technologies including Internet of Things sensors, cyber-physical systems, cloud-edge computing, artificial intelligence, augmented reality, and real-time analytics. Applications in machinery health monitoring, quality assurance, energy management, robotics coordination, supply-chain visibility, and adaptive production planning are analyzed. Particular attention is given to failure prediction, remaining useful life estimation, human-machine collaboration, sustainability performance, and resilience under disruptions. Benefits include reduced downtime, improved asset utilization, lower maintenance cost, faster innovation cycles, and safer operations. Major barriers include data interoperability, cybersecurity, model fidelity, integration with legacy equipment, workforce skill gaps, and governance concerns. A future roadmap is proposed involving cognitive twins, federated industrial intelligence, self-optimizing factories, and ethical AI oversight. The paper concludes that digital twin ecosystems can become the operating intelligence of Industry 5.0 by combining predictive insight, autonomous response, and human expertise within adaptive industrial environments.