Analysis And Cooling Suppression Of Thermal Error Sensitive Points Of Biaxial Rotary Milling Head

The five-axis linkage machine tool is the main processing equipment for processing complex curved parts,and is widely used in aerospace,shipbuilding,mold,high-precision instruments and other fields.The A/C-axis biaxial rotary milling head,which is the core component of medium and large-scale five-axis linkage machine tools,has a complex temperature field due to the comprehensive influence of unequal heat from multiple heat sources during the machining process.Therefore,each component produces different temperature rises,which in turn produces different degrees of thermal deformation,which ultimately affects the machining accuracy of the machine tool.In order to obtain higher machining accuracy,this paper studies the temperature field and thermal error of the swing head,and conducts cooling suppression for some sensitive positions that are prone to large thermal errors caused by temperature changes.Compared with some conventional methods of reducing thermal error,it has the advantages of low cost and wide application range.The main research contents include the thermal-structure coupling simulation analysis of the swing head,the thermal error sensitivity analysis algorithm,the thermal error suppression experiment and the swing head cooling simulation.First,the heat generation is obtained based on the performance parameters of the biaxial rotary milling head,and the convective heat transfer coefficient is obtained according to the heat transfer.A simplified three-dimensional model of the swing head is established,and the thermal-structural coupling analysis is carried out in combination with the finite element to obtain the temperature field and deformation field distribution of the swing head,which provides a theoretical basis for subsequent experiments and cooling simulations.Secondly,for the simulation of L-shaped parts,quantify the influence of different temperature detection points on the thermal error,and select the thermal error sensitive points that have a greater impact on the thermal error from multiple detection points.A variety of sensitivity analysis methods for neural networks are used to analyze the constructed thermal error prediction model,and the results of various sensitivity analysis methods are compared with the simulation results,and the most reliable method is selected.The subsequent experiment is divided into three parts to obtain the feasibility of the cooling scheme,the thermal error prediction model,and the verification of the thermal error sensitivity analysis algorithm.According to the distribution of the temperature field and deformation field of the biaxial rotary milling head,the position of the heat source and the detection position of the temperature and displacement sensor in the shell experiment are designed.Then the thermal error model is constructed by combining the BP neural network,the temperature and displacement data collected by the experiment.Sensitivity analysis algorithm is used to select thermal error sensitive points among multiple detection points for shell cooling suppression experiments,which effectively suppresses the thermal error of the shell.Finally,combining the biaxial rotary milling head simulation and the shell experimental design detection point location,the cooling simulation is carried out for multiple detection points.The results show that the cooling of the thermal error sensitive point can effectively suppress the thermal error of the swing head.The thermal error of the biaxial rotary milling head,the temperature field of cooling suppression and the deformation field of cooling suppression are further analyzed in depth.Then the cooling simulation and experiment of the biaxial rotary milling head are compared and analyzed,which provides the basis for further in-depth research.