AI-Enabled Digital Twin Framework for Predictive Maintenance in Autonomous Manufacturing
Keywords:
digital twin, predictive maintenance, autonomous manufacturing, artificial intelligence, edge computing, system governance, sustainability, fairnessAbstract
The convergence of artificial intelligence and digital twin technology offers transformative potential for predictive maintenance in autonomous manufacturing environments. This paper presents a comprehensive framework that integrates real-time data acquisition, machine learning-driven anomaly detection, and simulation-based decision support within a unified digital twin architecture. The framework is designed to address critical challenges in scalability, data heterogeneity, and operational uncertainty that characterize modern smart factories. We examine structural trade-offs between centralized and edge-based processing, the governance of multi-stakeholder data sharing, and the implications of model fidelity on maintenance scheduling. Sustainability considerations are explored through the lens of energy-aware computation and lifecycle extension of capital equipment. Robustness is analyzed in terms of adaptability to concept drift and sensor failures, while fairness and policy dimensions are discussed with respect to workforce displacement and algorithmic accountability. Through comparative case illustrations drawn from automotive assembly and semiconductor fabrication, we demonstrate the framework’s capacity to reduce unplanned downtime, optimize spare parts inventory, and improve overall equipment effectiveness. The paper concludes with forward-looking perspectives on federated learning for privacy-preserving collaboration and the integration of explainable AI to support human-in-the-loop oversight. This research contributes a systems-level blueprint for deploying AI-enabled digital twins that are technically robust, economically viable, and socially responsible.
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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.
