Prediction of spindle thermal deformation and displacement using back propagation neural network

Abstract

Over the years, machine tool manufacturers have moved steadily towards the enhancement of machining accuracy to improve the quality of finished products. In this study, the thermal deformation of a machine spindle, which has a profound effect on machining accuracy, was investigated. The temperatures of the front and rear spindle bearings, and of the environment as well as the Z-axis displacement on a model MC4200BL CNC lathe (Hybrid Sphere) were measured under long-term operating conditions. Measurements were carried out at spindle speeds of 1000, 1500, 2000, 2500, and 3000 rpm, and the data were used to establish a model for the prediction of spindle displacement. A back propagation neural network (BPNN) was used to establish the model and explore adjustments of the training function, the data training ratio, and the number of neurons in the hidden layer. Results of the experiments showed that the coefficient of determination (R2) of the prediction model derived from the best parameters can be up to 0.9948. This was much better than the 0.8273 achieved by the partial least squares regression method.