Research On Optimization Of 2A12 Aluminum Alloy High-speed Milling Parameters Based On Neural Network And Genetic Algorithm

With the advancement of technology and the use of a large number of high-speed,high-precision machine tools,the processing of aluminum alloy workpieces is developing in the direction of high speed.Selecting the optimal milling parameters that meet the requirements of high-speed milling of aluminum alloys is of great significance for giving full play to the performance of newly introduced machine tools,improving machine tool utilization,and ensuring production safety.2A12 aluminum alloy is widely used in the aerospace field because of its low strength,low hardness and good cutting performance.This thesis taken 2A12 aluminum alloy research object,used neural network and genetic algorithm to optimize high-speed milling parameters,the specific work was as follows:Firstly,this thesis established a simulation model for high-speed cutting of 2A12 aluminum alloy in ABAQUS,analyzed the influence of cutting parameters and tool parameters on cutting force through simulation methods,and designed single-factor milling experiments to study the effects of spindle speed and depth of cut on cutting force Influence and verified the simulation results.The results showed that the error between simulation and experiment results within 17%,which verified the availability of the simulation model.Secondly,by designing orthogonal milling experiments,this thesis obtained the values of three-directional milling force and surface roughness in the process of highspeed milling of 2A12 aluminum alloy.According to the method of regression analysis,the regression model of cutting force and surface roughness was obtained,and the regression result met the F test.Then,by comparing the experimental results with the regression results,it was found that the regression error was basically concentrated within 15%,which proved the effectiveness of the regression model.Thirdly,this thesis designed the structural parameters of the BP network,and established a neural network prediction model for milling forces and surface roughness based on the selected parameters.The comparison showed that the prediction effect of the neural network model was significantly better than the regression model.Finally,with the goal of maximum cutting efficiency and minimum surface roughness,based on the establishment of an ANN prediction model,this thesis used GA algorithm to optimize the parameters of high-speed milling 2A12 aluminum alloy,and designed the optimization of high-speed milling aluminum alloy parameters system.