Design optimization of motorized spindle bearing locations based on dynamic model and genetic algorithm

In this paper, an optimization method based on dynamic model and genetic algorithm is proposed for the design of motorized spindle bearing locations. Firstly, the dynamic model of motorized spindles is developed based on the Timoshenko beam model and Jones' quasi-static bearing model. Then, the developed dynamic model is validated with the hammer response test on a motorized grinding spindle system. Finally, the design optimization method is proposed by combining the dynamic model with genetic algorithm. In order to obtain higher rigidity, the optimal locations of bearings on the spindle are calculated with the genetic algorithm. The results show that the first mode natural frequency (FMNF) of the system increases by 12.38% than the original value after optimization.