Enhancing Energy Efficiency in Machining through Digital Twin Technology: Predictive Modeling of Thermal Loads in Machine Tool Spindles - Bani-Hani, Mohammad; Hanenkamp, Nico (Procedia CIRP, 2025)
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.
CRIS-Team Scopus Importer:2025-05-02
Keywords: Digital Twin; Machine Tools; Machining Optimization; Material Removal Simulation; Thermal Modeling

Innovative Refrigeration Technology for Machine Tools with Sustainable Refrigerants and Digital Twins - Bani-Hani, Mohammad; Stemmler, Timon; Hanenkamp, Nico (Procedia CIRP, 2024)
This article investigates the trend of assessing energy efficiency, sustainability and greenhouse gas emissions in the operation of 5- axis milling machine tools. This research aims to predict the cooling load by thermal models control within the main spindle system to ensure that cooling devices are dimensioned adequately and to keep it running under optimal conditions leading to improve the overall efficiency. In order to achieve this goal, a digital twin approach is currently being developed utilizing commercial software which consists of four primary models. A simulation model for material removal is used to predict the mechanical load required for the milling process to finish the CAD model. The second model is the spindle control model to determine the electric currents needed to operate the induction motor at the specified mechanical power. This model provides the electric current data to the lumped-parameter thermal networks (LPTN) model, which is proposed to accurately represent the heat dissipation from the motor and spindle systems towards the cooling channel in the main spindle system. Subsequently, the task at hand involves making an estimate of the necessary outlet temperature derived from the refrigeration unit model, which is essential for effectively covering the thermal energy produced by the spindle system.<br
Keywords: Digital Twin; Lumped-parameter Thermal Networks; Milling; Machine Cooling