AI-Driven Predictive Maintenance in Smart Manufacturing Using Edge-Enabled Digital Twin Architectures

Abstract

The increasing complexity of modern manufacturing systems under the Industry 4.0 paradigm has necessitated the adoption of intelligent maintenance strategies capable of enhancing system reliability and reducing operational costs. This paper presents an AI-driven predictive maintenance framework utilizing edge-enabled digital twin architectures for smart manufacturing environments. The proposed model integrates Internet of Things (IoT) sensors for real-time data acquisition, edge computing for low-latency processing, and digital twin technology for dynamic system modeling. Advanced machine learning techniques, including hybrid deep learning models, are employed to predict equipment failures with high precision. The system reduces dependency on cloud infrastructure by enabling localized decision-making at the edge, thereby improving response time and data security. Experimental evaluation demonstrates that the proposed framework achieves prediction accuracy exceeding 96%, reduces unplanned downtime, and enhances maintenance efficiency. The digital twin provides continuous synchronization between physical assets and virtual replicas, facilitating real-time monitoring and simulation. The study addresses key challenges such as scalability, interoperability, and cybersecurity, offering a comprehensive solution for next-generation industrial systems. The results highlight the transformative potential of integrating AI, edge computing, and digital twins in predictive maintenance applications.