Thermal Error Modeling and Compensating of Motorized Spindle Based on Improved Neural Network
In a lot of factors, thermal deformation of motorized high-speed spindle is a key factor affecting the manufacturing accuracy of machine tool. In order to reduce the thermal errors, the reasons and influence factors are analyzed. A thermal error model, that considers the effect of thermodynamics and speed on the thermal deformation, is proposed by using genetic algorithm-based radial basis function neural network. The improved neural network has been trained and tested, then a thermal error compensation system based on this model is established to compensate thermal deformation. The experiment results show that there is a 79% decrease in motorized spindle errors and this model has high accuracy.
Xie Yi and Li Mi
C. L. Lei and Z. Y. Rui, "Thermal Error Modeling and Compensating of Motorized Spindle Based on Improved Neural Network", Advanced Materials Research, Vols. 129-131, pp. 556-560, 2010