Research On Milling Process Of 15-5PH Stainless Steel For Civil Large Aircraft

15-5PH stainless steel is a new type of stainless steel,which has been widely applied to corrosion resistance and force bearing structure in large aircraft,for fuselage girder,driving device and undercarriage due to excellent mechanical property and high temperature,corrosion resistance properties.However,properties of low heat conductivity coefficient and elasticity modulus also lead to excessive cutting force,high cutting temperature,fierce tool wear and serious work hardening in the process of milling,which results in low milling quality and high processing cost.Hence,study the machinability of 15-5PH stainless steel,optimize milling parameters and develop cutting database with independent intellectual properties play a practical significance on increase of milling efficiency and control of processing cost.According to the functional requirement of database system,roughing milling,arc surface finishing and web plate finishing are taken as objects to conduct the research on measuring of tool wear,tool life and surface roughness,establishment of experimental results prediction models and optimization of milling parameters.At last,all the experimental data is entered into database system.Firstly,considering tool wear and tool life on rough milling of 15-5PH stainless steel,single factor experiment on milling process is conducted.Then,the factor effect on both of them are analyzed.Meanwhile,BP Neural Network is applied to construct the prediction model for tool life.Finally,the prediction model is integrated into particle swarm optimization(PSO)algorithm to find the optimal parameters for the maximum material removal rate.Secondly,to evaluate the influence of milling parameters on surface roughness of arc surface,research on milling process is conducted by orthogonal experiment design method.Range analysis is applied to evaluating the influence of various milling parameters on surface roughness.Meanwhile,based on Partial least squares(PLS)and kinematics analysis,an empirical prediction model as well as a theoretical prediction model of surface roughness are proposed.Comparison of prediction accuracy between them reflects that the theoretical model presents better performance.Then,the theoretical model is integrated into particle swarm optimization algorithm to find the optimal parameters for the maximum material removal rate.Thirdly,high-speed milling experiments on web plate are conducted by using orthogonal experiment design method.Range analysis is applied to evaluating the influence of various milling parameters on surface roughness.A BP Neural Network prediction model established by referring to experimental data is proposed.Then,the particle swarm optimization algorithm is employed to find the optimal parameters for maximum material removal rate.Lastly,all the experimental data above are enter into aviation cutting database system,providing technical supporting for it core function.