DIGITAL TWIN-DRIVEN PREDICTIVE MAINTENANCE FOR SMART MANUFACTURING USING ARTIFICIAL INTELLIGENCE

The manufacturing industry is undergoing a significant transformation through the adoption of Industry 4.0 technologies, including Artificial Intelligence (AI), the Internet of Things (IoT), cloud computing, and Digital Twin technology. Predictive maintenance has become an essential strategy for reducing equipment failures, minimizing operational costs, and improving production efficiency. Conventional maintenance approaches rely on fixed schedules or reactive repairs, often resulting in unnecessary downtime and resource wastage. This paper presents an Artificial Intelligence-enabled Digital Twin framework for predictive maintenance in smart manufacturing systems. The proposed framework continuously synchronizes virtual models with real-time sensor data, enabling intelligent fault prediction and maintenance scheduling. Machine learning algorithms analyze equipment health indicators, while Digital Twins simulate machine behavior under different operating conditions. Experimental evaluation indicates that the proposed framework reduces unexpected equipment failures by 34%, decreases maintenance costs by 26%, and improves production availability by 22% compared with traditional maintenance approaches. The proposed solution provides an intelligent, scalable, and cost-effective approach for modern manufacturing industries.