Monitoring and Prediction of the Real-Time Transient Thermal Mechanical Behaviors of a Motorized Spindle Tool

The spindle is a critical component that significantly influences the performance of machine tools. In motorized spindles, heat generation from both the bearings and built-in motor leads to thermal deformation of structural components, which, in turn, affects machining accuracy. This study investigates the thermo-mechanical behavior of motorized spindles under various operational conditions, with the aim of accurately predicting thermally induced axial deformation and determining optimal temperature sensor placement. To achieve this, temperature rise and deformation data were simultaneously collected using appropriate data acquisition systems across varying spindle speeds. A correlation analysis confirmed a strong positive relationship exceeding 97.5% between temperature rise at all sensor locations and axial thermal deformation. Multivariate regression analysis was then applied to identify optimal combinations of sensor data for accurate deformation prediction. Additionally, a finite element (FE) thermal–mechanical model was developed to simulate spindle behavior, with the results validated against experimental measurements and regression model predictions. The four-variable regression model and FE simulation achieved Root Mean Square Errors (RMSEs) of 0.84 µm and 0.82 µm, respectively, both demonstrating close agreement with experimental data and effectively capturing the trend of thermal deformation over time under different operating conditions. Finally, an optimal sensor configuration was identified that minimizes pre-diction error while reducing the number of required sensors. Overall, the proposed methodology offers valuable insights for optimizing spindle design to enhance thermal–mechanical performance.