AI-Driven Predictive Maintenance for Smart Manufacturing Systems Using Digital Twin Technology

The rapid advancements in Industry 4.0 and smart manufacturing systems have necessitated the integration of Artificial Intelligence (AI) and Digital Twin Technology (DTT) to enhance operational efficiency and predictive maintenance strategies. This study proposes an AI-driven predictive maintenance framework that leverages Digital Twin Technology to enable real-time monitoring, fault diagnosis, and failure prediction in industrial environments. The framework integrates machine learning (ML) models, deep learning techniques, and edge computing to analyze sensor data, detect anomalies, and optimize maintenance schedules. A reinforcement learning-based decision model is employed to dynamically adjust maintenance strategies, reducing downtime and extending equipment lifespan. Additionally, physics-informed AI models are incorporated into the digital twin architecture to simulate operational behaviours and predict potential failures with high accuracy. The proposed system is validated through a case study in a smart manufacturing plant, demonstrating a 35% improvement in predictive accuracy, 40% reduction in unplanned downtimes, and 25% optimization in maintenance costs compared to traditional predictive maintenance approaches. The findings indicate that the integration of AI and DTT significantly enhances the reliability and efficiency of cyber-physical manufacturing systems (CPMS), paving the way for more autonomous and intelligent industrial operations.