Research On Thermal Error Analysis And Modeling Of Horizontal Machining Center

For precision machine tools,the thermal error caused by the thermal characteristics of the machine tool accounts for 40 %-70 % of the total error of the machining center.Therefore,it is of great significance to analyze the influence mechanism of thermal error on the machining accuracy of the machine tool and the temperature change trend of each part of the machine tool for guiding the machine tool designers to carry out the heat source layout and structural optimization design of the machine tool.Thermal error modeling of machine tool is an important research direction of machine tool precision control.It is very important to improve the machining accuracy and stability of the machine tool.Therefore,this paper conducts theoretical analysis and finite element simulation analysis on the thermal error of a horizontal machining center,establishes a thermal error model,and analyzes the improvement of machine tool accuracy.Firstly,the knowledge of thermodynamics is introduced.According to the heat conduction equation,heat conduction coefficient,heat capacity and thermal conductivity,the heat generation model and heat dissipation model are established.When the heat generation model is established,the influence of heat source such as spindle rotation,cutting force,friction and thermal stress of machine tool structure is considered.When the heat dissipation model is established,the influence of surface heat dissipation of machine tool components and heat transfer process of surrounding environment on it is mainly considered.In addition,the equivalent mechanical load model of the main contact pair and the contact thermal resistance model are established.Increasing the calculation of the actual contact area of the contact thermal resistance can make the finite element simulation more accurate.Secondly,the heat transfer coefficient and heat conductivity of the thermal boundary conditions are calculated by the heat generation model and the heat dissipation model,so as to determine the boundary conditions of the heat source such as the machine tool spindle and the guide rail slider.The boundary conditions and other parameters are input on the Workbench platform to analyze the temperature cloud map of the horizontal machining center and the temperature distribution of the corresponding structural large parts.The maximum temperature of the horizontal machining center is 51.996 °C.The deformation field is analyzed under the conditions of gravity load,thermal load,gravity load and thermal load.The results show that the deformation caused by thermal load and the deformation caused by gravity load are linearly independent of each other,and the linear superposition is the deformation under the combined action of thermal load and gravity load.Then the thermal characteristics of the machine tool are optimized,mainly in the aspect of heat dissipation.The heat dissipation effect of the machine tool under different cooling water flow is compared.The results show that the economic benefit is the best when the cooling water flow is 10 L /(min).Then,according to the selection theory of temperature measuring points,the temperature measuring points are preliminarily selected,and the thermal key points are identified according to the temperature rise data and axial and radial error data of 18 temperature measuring points.The Pearson correlation coefficient of each measuring point was calculated,and 8 thermal key points were selected by fuzzy c-means clustering analysis.Finally,the wavelet neural network is used to establish the thermal error model of the machine tool.The genetic algorithm is combined with the wavelet neural network.The genetic algorithm is used to make up for the shortcomings of the global search of the wavelet neural network.The wavelet basis function is used as the hidden layer of the neural network.The replaced wavelet neural network has multi-scale resolution,strong function approximation ability and good time-frequency local characteristics,which improves the slow convergence speed of the neural network and is easy to fall into the local optimal solution.In order to verify the prediction accuracy of the optimized thermal error model,it is compared with the model established by multivariate linear fitting.The results show that the goodness of fit of the axial thermal error of the wavelet neural network optimized by genetic algorithm is 0.923,which is15.3 % higher than that of the multivariate linear regression.The results of this study have certain guiding significance for the design and manufacture of machine tools.By better understanding the sources and effects of thermal errors,machine tool manufacturers can develop more effective methods to reduce thermal errors,which is of great significance for improving the accuracy and stability of machine tools.