AI-Enhanced Digital Twin Systems for Industrial Automation
Keywords:
Digital twins, industrial automation, artificial intelligence, cyber-physical systems, edge computing, predictive maintenance, industrial IoT, system architecture, autonomous systems, socio-technical governanceAbstract
AI-enhanced digital twin systems have emerged as a transformative paradigm in industrial automation, enabling real-time synchronization between physical assets and their virtual counterparts through data-driven intelligence and large-scale system integration. This paper investigates the architectural, computational, and governance dimensions of integrating artificial intelligence into digital twin ecosystems for industrial environments characterized by complexity, heterogeneity, and stringent operational constraints. We analyze how AI augments digital twins through predictive modeling, adaptive control, anomaly detection, and autonomous decision support while emphasizing system-level trade-offs involving latency, scalability, robustness, and interpretability. The study further examines infrastructure requirements spanning edge-cloud continuum architectures, industrial IoT deployments, and interoperable data fabrics that enable continuous synchronization between physical systems and virtual representations. Special attention is given to the socio-technical implications of AI-driven autonomy in industrial environments, including workforce transformation, safety assurance, accountability frameworks, and regulatory alignment. Through comparative conceptual analysis across manufacturing, energy systems, and logistics networks, we identify critical design patterns and persistent challenges in deploying AI-enhanced digital twins at scale. The paper concludes by outlining future research directions focused on resilient architectures, federated intelligence, and sustainable industrial automation ecosystems.
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