HMC500 Horizontal Machining Center Spindle Thermal Error Analysis And Modeling

Thermal error is one of the main error sources of CNC machine tools.The errors caused by temperature changes and uneven distribution have great influence on the precision of ultra-precision machine tools.Thermal problems have become an important factor influencing the precision of precision machine tools.Spindle system as the most important part of CNC machine tools,it's thermal deformation caused by the heat generated and the rotation is the main source of machining accuracy error.Therefore,reducing the thermal error is the key to improving the CNC machining accuracy.Then the error compensation method has been most commonly used to reduce the thermal error,but the realization of thermal error compensation requires the actual measurement value of the temperature sensor to calculate the compensation value through the error model and feeding it back to the compensation actuator.Therefore,the establishment of thermal error prediction mathematical model is the most critical step for the thermal error compensation control technology of CNC machine tools,and is also the most complicated and difficult work.This thesis takes the HMC500 spindle system as the research object,and uses fuzzy clustering and gray correlation degree theory to optimize the temperature measurement point of the spindle system to the machine tool.Based on this the theory,fuzzy neural network method is used to model and predict the thermal error of the spindle system to the machine tool,which provides important theoretical support for the future thermal error compensation technology.The main research contents of this thesisr are as follows:(1)The subject's goal,background and significance of researching,the domestic and international investigative review about dynamics of temperature measurement point arrangement optimization technology,and thermal error modeling method are introduced.(2)A detailed structural description and the key parts of the machine tool are provided.(3)Based on the basic theory of fuzzy clustering method-gray correlation degree,an experimental platform is set up.The preliminary relationship between temperature and main shaft thermal deformation is obtained through testing.The sensor is selected by fuzzy clustering-gray correlation method and the optimal temperature measurement point for thermal error modeling is selected to reduce the number of temperature variables in the thermal error model,thereby the thermal error modeling efficiency is improved and the thermal error measurement points of the spindle system is optimized.(4)The finite element analysis software ANSYS Workbench17.0 was used to perform thermal analysis on the HMC500 spindle system.The temperature field distribution and thermal deformation laws of the spindle system were obtained,and the thermal deformation of the spindle system was simulated and calculated.By comparing the simulation results with experimental data,the numerical values are basically consistent,which verifies the effectiveness of the fuzzy clustering-gray correlation method for selecting key points.(5)Based on the relationship between the temperature of the key point of the numerical control machine tool spindle and the thermal deformation of the spindle,the fuzzy neural network T-S is used to establish the spindle thermal error model,and the thermal error of the spindle system is predicted.The experimental results show that the fuzzy neural network prediction model can accurately predict the thermal error of the spindle system and provide reference for the thermal error compensation of CNC machine tools.