Thermal Characteristics Analysis And Thermal Error Compensation Method Of High-speed Milling Motor Spindle

With the improving requirements of processing precision and processing efficiency for NC machine tools, the application of motor spindle is becoming more and more common, as the motor spindle will generate heat which has obvious effect on processing accuracy of NC machine tools, the research about thermal deformation caused by the motor spindle heat and how to compensate the thermal deformation is of great significance for improving the accuracy of NC machine tools. Focusing on analyzing and researching the thermal characteristic of high-speed milling motor spindle and thermal error prediction model, the following works were carried out in this paper.(1) Structure and main heat source of motor spindle was analyzed through taking high-speed milling motor spindle as object. According to the heat transfer theory, characteristics of heat transfer in motor spindle were furtherly studied. According to the way and characteristic of heat transfer, the generating rate of heat of motor spindle heat sources and boundary conditions such as convection were minutely calculated. The heat transfer mechanism of spindle bearings which can produce and transfer heat was researched particularly. The thermal resistance network was established according to the energy conservation theory and main heat transfer parameters such as bearing contact thermal resistance by using differential equation.(2) Heat-state analysis model of thermal resistance of bearing contact area in motor spindle was set up. Temperature field and thermal deformation law of motor spindle were deeply studied with the finite element method. Influence of speed on the motor spindle temperature was studied. Law of thermal displacement of motor spindle and thermal deformation in the front of motor spindle and its effect on the machining accuracy were discussed. The method reducing the thermal deformation influence of motor spindle units through improving the lubrication method of spindle bearing put forward.(3) The motor spindle test platform was set up and the method that arranges the temperature sensor using the motor spindle temperature field distribution law was put forward. According to the experimental data, the multiple linear regression method was adopted to establish the thermal error compensation model, the prediction accuracy and stability between binary linear regression model and binary quadratic linear regression mode was compared at the same time, the method of using RBF neural network to set up the thermal error compensation model was presented. And the precision of RBF neural network was evalued, in addition this method was compared with linear regression thermal error compensation model.The following conclusions were obtained through researching:(1) Bearing speed has a great influence on the temperature field, temperature rises with the increase of speed, temperature amplitude is relatively larger after the speed rise to 6000 r/min, and thermal gradient in the bearing is affected by the thermal resistance in the contact zone, internal temperature difference rises with the increase of rotational speed.(2) Cooling system in motor stator has obvious effect on cooling and homogenizing the temperature field of motor spindle. The measured value of bearing temperature and stator temperature is close to the simulation value under low and middle speed. Through simulation analysis, change bearing lubrication method can effectively reduce the bearing temperature, and then reduce the thermal deformation of motor spindle. Axial thermal displacement is the maximum in the front of spindle and change obviously before the temperature field is steady.(3) When establish the multivariate linear regression thermal error model, prediction accuracy and stability of binary quadratic linear regression model are both better than the binary linear regression model. Multivariate linear regression forecast model has good prediction accuracy for radial thermal displacement, but it has poor robustness and low displacement compensation rate for the larger axial displacement. Compared with multiple linear regression model, RBF neural network model is better both on the accuracy and stability, and it’s more suitable for setting up motorized spindle thermal error compensation model.