A Study on the Thermal Deformation Prediction Model of a Motorized Spindle Based on Swarm Intelligence Optimization Algorithms

Thermal deformation of the motorized spindle significantly impacts the machining accuracy of CNC machine tools. The thermal expansion coefficient, a critical parameter affecting thermal deformation, is highly nonlinear, making it challenging to determine an accurate coefficient that enhances machining precision. This study proposes a thermal deformation model for the motorized spindle based on an improved Grasshopper Optimization Algorithm, aimed at optimizing the thermal expansion coefficient. Experimental studies were conducted on the motorized spindle at various rotational speeds. The results demonstrate that the thermal expansion coefficient obtained using the improved Grasshopper Optimization Algorithm yields more accurate predictions of thermal deformation, with smaller errors compared to actual measurements, than those obtained using an empirical thermal expansion coefficient.