Research On Tool Wear And Workpiece Surface Roughness In The Process Of Spiral Bevel Gear Milling

With the development of aerospace,shipbuilding and automobile manufacturing industries,the research and application of spiral bevel gears have gradually attracted wide attention.Spiral bevel gears are widely used in mechanical transmission devices because of their compact structure,strong bearing capacity and high coincidence degree.At present,the commonly used material of spiral bevel gear is20 Cr Mn Ti,and its characteristics of high hardness,strong chemical reactivity and poor heat dissipation make the machinability of end-face milling spiral bevel gear poor.Large cutting force and high cutting temperature easily lead to serious tool wear,poor workpiece processing quality and low processing efficiency,which seriously affect cutting efficiency and tool life.In the process of spiral bevel gear machining,reasonable cutting parameters can not only improve tool life,workpiece surface roughness,but also improve machining efficiency.RBF neural networkNSGA genetic algorithm is used to optimize the cutting parameters of end-milling spiral bevel gears,and the cutting scheme with minimum tool wear and optimal surface roughness is explored.Firstly,according to the milling principle of generating method to process spiral bevel gears,the cutting position relationship is determined.The structure of spiral bevel gear is analyzed,and the equations of concave and convex cutting surfaces are deduced.Based on the cutting surface model,the mathematical equation of tool cutting path is obtained,and the motion path and wear form of machining tool are discussed,and the mathematical model of tool wear is established.The mathematical model of tool wear is visualized by Matlab software,and the influence of cutting parameters on tool wear is analyzed.The formation mechanism of residual height of machined workpiece surface is explored,and the mathematical model of surface roughness is established,which provides theoretical basis for the subsequent research of surface roughness of spiral bevel gears.Secondly,under the condition of high-speed dry cutting,the cutting process and cutting tools of spiral bevel gears are simulated and analyzed.CATIA software is used to build the three-dimensional model of spiral bevel gear and cutter,and DEFROM simulation software is used to simulate and analyze the end milling spiral bevel gear,and the changing law of cutting force and cutting temperature in the cutting process is discussed.Explore the wear form and mechanism of cutting tools and the change law of machined workpiece surface deformation in the cutting process.The change of workpiece surface deformation can reflect the actual machined workpiece surface roughness,and provide technical support for the experimental research of end milling spiral bevel gear under the condition of highspeed dry cutting.Furthermore,the cutting experiment of spiral bevel gear is carried out by face milling.The tool wear was detected by ultra depth of field microscope,scanning electron microscope and energy spectrometer,and the wear forms and wear mechanisms of tools under different cutting parameters were analyzed,and the change rules of tool wear under different cutting parameters were discussed.P40 rolling inspection machine is used to detect the surface roughness of workpiece,and the variation law of machined surface roughness of spiral bevel gear under different cutting parameters is explored.Finally,on the basis of experiments,NSGA-II genetic algorithm based on RBF neural network approximation model is used to optimize the cutting parameters of spiral bevel gears.Selecting feed rate and cutting speed as inputs,minimum tool wear,minimum surface roughness and maximum machining efficiency as outputs,the multi-objective Pareto frontier diagram and the relationship between different objectives are obtained,and the optimal cutting parameters are discussed and determined.It provides some technical reference for reducing tool wear,surface quality of workpiece and improving machining efficiency under the condition of high-speed dry cutting.