AI-Enabled Digital Twin Framework for Predictive Maintenance in Smart Manufacturing Systems
Keywords:
digital twin, predictive maintenance, artificial intelligence, smart manufacturing, cyber-physical systems, edge computing, model governance, sustainability, robustnessAbstract
The convergence of artificial intelligence and digital twin technology presents a transformative paradigm for predictive maintenance within smart manufacturing systems. This paper develops a comprehensive framework that integrates AI-driven analytics with real-time digital representations of physical assets to enable proactive fault detection, remaining useful life estimation, and optimized maintenance scheduling. The proposed architecture emphasizes system-level considerations including data fusion across heterogeneous sensor networks, edge-cloud orchestration for low-latency inference, and governance mechanisms that ensure model transparency and fairness. A critical examination of structural trade-offs reveals tensions between model complexity and computational efficiency, between centralized and decentralized data management, and between predictive accuracy and generalizability across different production environments. The framework further addresses sustainability imperatives by incorporating energy-aware scheduling and lifecycle costing within the maintenance decision loop. Through cross-domain comparisons with aerospace and energy sectors, the paper highlights domain-specific adaptation requirements and lessons for manufacturing settings. Robustness and resilience are treated as first-class design properties, with fault-tolerant data pipelines and adversarial robustness of AI models considered. Governance and policy implications are discussed, focusing on accountability, data sovereignty, and the need for standardized interoperability protocols. The paper concludes by outlining open research challenges in federated learning for multi-site digital twins, human-in-the-loop validation, and the integration of real-time economic dispatch with maintenance planning. This work contributes a structured, socio-technically aware blueprint for deploying AI-enabled digital twin frameworks that are not only technically sound but also operationally sustainable and ethically aligned.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
