Enhancing Energy Efficiency in Machining through Digital Twin Technology: Predictive Modeling of Thermal Loads in Machine Tool Spindles

Improving energy efficiency in machining processes is a critical objective in state-of-the-art-manufacturing. The spindle system of machine tools, a primary source of thermal loads, plays a significant role in determining energy efficiency. Excessive heat generation not only affects spindle performance but also increases energy demand, particularly for cooling systems. This study introduces a digital twin-based predictive framework designed to optimize cooling loads in machine tool spindles by integrating advanced mechanical and thermal modeling techniques. The framework leverages predictive models to accurately estimate mechanical load profiles, which are derived from machining parameters such as tool paths, cutting forces and feed rates. These mechanical profiles are coupled with thermal models to simulate heat generation and dissipation dynamics within the spindle system. By integrating these sub-models, the framework enables precise prediction of cooling requirements, ensuring that the spindle operates within optimal temperature ranges without unnecessary energy consumption.