Abstract
Thermal error stability (STE) of the spindle determines the machining accuracy of a precision machine tool. Here we propose a thermal error feedback control based active cooling strategy for stabilizing the spindle thermal error in long-term. The strategy employs a cooling system as actuator and a thermal error regression model to output feedback. Structural temperature measurements are considerably interfered by the active cooling, so the regression models trained with experimental data might output inaccurate feedbacks in unseen work conditions. Such inaccurate feedbacks are the major cause for excessive fluctuations and failures of the thermal error control processes. Independence of the thermal data is analyzed, and a V-C (Vapnik-Chervonenkis) dimension based approach is presented to estimate the generalization error bound of the regression models. Then, the model which is most likely to give acceptable performance can be selected, the reliability of the feedbacks can be pre-estimated, and the risk of unsatisfactory control effect will be largely reduced. Experiments under different work conditions are conducted to verify the proposed strategy, the thermal error is stabilized to be within a range smaller than 1.637μm, and thermal equilibrium time is advanced by more than 78.3%.
Feedback control effect on the Lotka-Volterra prey-predator system with discrete delays
