Study On The Thermal Characteristics And Thermal Error Prediction Method Of The Bearings In Feed Systems

Ball screws are widely used in various types of precision equipment such as machine tools.Generally,the machining accuracy is greatly affected by the temperature rise of the system.Among the main heat sources of the machine tool feed system,the bearing generates the largest amount of heat,so it is of great significance to study the thermal characteristics of the screw bearing and predict the thermal error in conjunction with the bearing temperature.In this paper,factors such as the centrifugal force of the bearing and the moment of the gyro are deduced,and the quasi-static equilibrium equation of the ball load is derived.Considering the contact thermal resistance between the bearing thermal nodes and the viscosity-temperature effect of the lubricant,a transient thermal network model of the angular contact ball bearings of the machine tool feed bearing system in pairs is established,and the transient thermal network model is derived using the difference method.The numerical calculation method is compared,and the numerical calculation results are compared with the finite element calculation results and experimental results,which verifies the effectiveness of the numerical algorithm.Based on the detection temperature of the feed system,an artificial neural network thermal error empirical prediction model based on genetic algorithm optimization is proposed.The specific research content is as follows.(1)Considering the combined effects of axial preload,centrifugal force and gyro moment on the bearings at both ends of the ball screw,a nonlinear pseudo-static model of the feed system bearing is established;the Newton-Raphson method is used to solve the nonlinear pseudo-static The mechanical model has proved the influence of the bearing feed speed and axial force on the contact angle between the ball and the inner and outer ring of the bearing and the ball spin angular velocity.(2)Using the thermal network method,a numerical calculation model of the transient temperature of the paired installation of angular contact ball bearings in the machine’s feed shaft system was established;it was determined based on the operating parameters of the feed system,the viscosity-temperature effect of the lubricant,and the component structure parameters.The thermal resistance between the networks,the heating rate of the bearing,and the thermal boundary conditions;through numerical calculations,the effect of the feed rate on the temperature rise of key points on the bearing surface was proved,and experimental verification was performed.(3)Transient thermal simulation analysis of angular contact ball bearings of the ball screw feed system.The finite element software ANSYS Workbench was used to analyze the temperature field of the bearings under different working conditions.Considering that the bearing was rotating at 5m/min,10m/min and 15m/min,the change of the system’s heating value and the real-time change of the ambient temperature in the bearing,the transient temperature of each node is analyzed,the errors of the finite element method and mathematical model,and the effect on the temperature of the screw are discussed.(4)Using empirical thermal error prediction technology and using the test results,the main influencing factors of the thermal error of the feed system are determined:the surface temperature of the left and right bearing seats,the surface temperature of the wire mother and the position of the screw,and it is used as the input node of the neural network The thermal error is the output node.The BP neural network,RBF neural network,and GRNN neural networks are trained separately.The output accuracy,operation time,and stability of the neural network are compared and analyzed.The evolutionary algorithm is used to optimize the neural network parameters.The test verified the prediction result of thermal error.