AI-Powered CNC Digital Twin for Predictive Maintenance

In today’s manufacturing landscape, maintaining the operational efficiency of CNC (Computer Numerical Control) machines is critical for minimizing downtime and maximizing productivity. This paper presents a novel framework that integrates artificial intelligence (AI) with digital twin technology for predictive maintenance in CNC operations. By leveraging IoT (Internet of Things) sensors, real-time data is collected from CNC machines, enabling the creation of a dynamic digital twin that accurately represents machine performance and behavior. Hereby, employing advanced machine learning techniques, specifically XGBoost, to analyze historical and real-time data, facilitating the early detection of potential failures and optimizing maintenance schedules. It not only enhances the accuracy of maintenance predictions but also significantly reduces the maintenance cost and unplanned machine downtimes. This research contributes to the ongoing development of smart manufacturing systems and underscores the importance of AI-driven solutions in enhancing predictive maintenance strategies within the industrial sector.