Development and optimization of a predictive maintenance model for CNC tooling systems

The rapid advancements in manufacturing technologies have led to the increased adoption of Computer Numerical Control (CNC) systems, particularly in precision machining. The performance of these systems is heavily reliant on the condition of the tooling, which deteriorates over time due to wear, mechanical stresses, and thermal effects. Predictive maintenance (PdM) plays a crucial role in reducing unexpected failures and minimizing downtime. This research presents the development and optimization of a predictive maintenance model for CNC tooling systems, utilizing machine learning algorithms and sensor data. The model is designed to forecast tool wear and failure, enhancing tool life prediction, improving machining efficiency, and reducing maintenance costs. Experimental results show that the proposed model can achieve high accuracy in tool condition prediction, which is crucial for proactive maintenance strategies.