Research On Thermal Error Test And Compensation Of Electric Spindle In Five-axis Machining Cente

The manufacturing level of high-end machine tools represents a country’s manufacturing level,and machine tool error is an important problem that hinders the development of manufacturing.Many scholars in the industry have shown that thermal error is the largest error source of CNC machine tools,accounting for about 40%to 70%of the total error.Among the total thermal errors,the thermal error caused by the motorized spindle system reaches about 50%.Due to the structural characteristics of"zero transmission",the motorized spindle system has caused problems such as high heat source intensity and difficulty in heat dissipation.The negative impact on the machining of the spindle caused by the accumulation of heat inside the motorized spindle is a major focus of current research in the industry,and it is the industry’s current one of the urgent problems to be solved.In this paper,the motorized spindle system of a five-axis machining center uses a multi-channel acquisition instrument and an American lion rotary error analyzer to conduct temperature field and thermal error detection experiments.By perfecting the existing self-built multi-channel acquisition equipment of the research group,automatic high-frequency acquisition can be realized,and a large amount of temperature field and thermal error data of the motorized spindle can be obtained;The two experiments establish different types of thermal error prediction models according to their own data characteristics,and can confirm the reliability of the experimental data.According to the characteristics of temperature field and thermal error data of motorized spindle measured by multi-channel acquisition instrument,fuzzy C-means clustering（FCM）algorithm is used to cluster and group the temperature measurement points to reduce the multicollinearity and data redundancy among temperature variables.The correlation degree between temperature measurement point and thermal error is calculated by grey correlation degree analysis（GRA）,and the measurement point with the largest correlation degree is selected as the key measurement point.According to the screened data,the whale optimization algorithm（WOA）is used to optimize the support vector regression model（SVR）,and the 10000r/min axial thermal elongation prediction model of motorized spindle is established.Meanwhile,SVR and multiple linear regression model（MLR）are established to compare and analyze the prediction effect of the model.According to the equipment conditions of the motorized spindle of the five-axis machining center at this stage,in order to establish a thermal error compensation model that is more general,easy to implement,effective and suitable for multiple different speeds,the temperature field and thermal error data collected by the rotary error analyzer are used.Based on the exponential function,a multivariable axial thermal elongation prediction model of the motorized spindle with respect to time,motor temperature and rotational speed is established.And the actual compensation effect of the model is verified through the idling compensation experiment,the plane machining compensation experiment,the S piece machining compensation experiment,and the impeller blade machining compensation experiment.In order to improve the thermal performance of motorized spindle,reduce the thermal deformation,firstly,heat source analysis is carried out on motorized spindle and thermal boundary conditions are calculated.Then,the steady-state thermal analysis and thermal-structure coupling analysis of motorized spindle are carried out by ANSYS Workbench software,and the temperature field and thermal deformation cloud maps of motorized spindle are obtained.The material selection of the main shaft is optimized,and the analysis results of ordinary 20Cr Mo,38Cr Mo Al,Zr O₂,Si₃N₄ and glass-ceramic main shafts are compared and analyzed,and the spindle material with better thermal performance and smaller thermal elongation is found.