Thermal deformation prediction of high-speed motorized spindle based on biogeography optimization algorithm

A thermal deformation prediction model of a high-speed motorized spindle is important for improving machining accuracy and reducing the thermal error in the spindle. The convective heat transfer coefficient reflects the internal heat exchange capacity of a motorized spindle. In the finite element thermal analysis of a motorized spindle, the heat transfer coefficient is used as the boundary condition to calculate the temperature field and thermal deformation. The accuracy of the convective heat transfer coefficient has the most evident effect on the thermal deformation prediction of the motorized spindle. In this paper, a method for optimizing the heat transfer coefficient based on the biogeography optimization algorithm is proposed for the 100MD60Y4 motorized spindle. The proposed method is used to develop a thermal deformation prediction model of an intelligent accurate motorized spindle. Accurate prediction of the thermal deformation of the motorized spindle is realized using the experimental data of the surface temperature of the motorized spindle. Experimental results show that the average prediction error after the optimization of the spindle bearing temperature is 0.22 °C. The average prediction error in the thermal deformation of the spindle is 0.72 μm. The developed model is more accurate compared with the conventional thermal deformation prediction model of the motorized spindle.