Research On Big Data Analysis And Influence Mechanism Of Error Sources In Ultra-precision Flycutting Machine Tools

The ultra-precision flycutting technology is widely used in the fields of military and national defense,precision instruments,laser fusion for its good machining accuracy,repeatable machining characteristics and one-time machining to form the nanometer precision surface of the workpiece.Due to the material characteristics of KDP crystals such as anisotropy,softness,fragility,and deliquescent,ultra-precision flycutting technology is currently the most effective method to process large-diameter KDP crystals.When the machine tool is processing,under the action of various factors such as the dynamic characteristics,machine tool structure,and thermal characteristics,the errors generated will affect the accuracy of the machine tool.Therefore,this paper analyzes the machine tool error source data,and focuses on the influence of temperature on the accuracy of machine tool.First,sensors such as temperature and vibration are arranged on the machine tool,and the status signal of the machine tool is collected through an intelligent monitoring system.The common evaluation indicators for signal feature extraction and common feature reduction methods are introduced.Based on this,the machine tool temperature signal and vibration signal are preprocessed.The results show that in the existing processing environment,the machine tool vibration has little effect on the GRMS value of the workpiece.The temperature has a certain correlation with the fluctuation of the GRMS value.Secondly,the basic theory of HMM is introduced,and the algorithms used by the model in solving evaluation problems,decoding problems and learning problems are discussed.Combining information fusion technology,two methods are proposed:Hidden Markov model based on feature fusion and Hidden Markov model based on decision fusion.The above two models are used to train the multi-channel temperature signal to obtain the temperature signal and the surface of the workpiece.The correlation degree of GRMS value is 92%and 90.5%respectively,verifying the influence of machine tool temperature on GRMS value.Thirdly,the thermal error of the machine tool is analyzed,the corresponding thermal conductivity differential equation is established,the internal heat source of the cutting machine tool is analyzed and determined,and the heat generation rate and convective heat transfer coefficient of the machine tool are calculated through the theory of heat generation and heat transfer.The finite element simulation model of the machine tool was established,and the temperature field distribution of the machine tool under the action of the internal heat source was solved.The result was compared with the data collected by the monitoring system to verify the accuracy of the simulation.Finally,through the thermal-structural coupling analysis of the machine tool,it is found that the maximum thermal deformation of the machine tool is 12.19μm,and the maximum deformation in the Y-direction of the processing sensitivity is 6.77μm,both of which are located at the spindle motor.The thermal deformation is finally applied to the tool tip through the spindle and beam.Above,it is determined that the thermal error of the machine tool is 1.42μm.At the end of the paper,in order to improve the machining accuracy of the machine tool,corresponding thermal optimization measures are proposed.