Optimization Of Process Parameters For CNC Milling Process Of Aluminum-lithium Alloy

With the continuous development and expansion of China's space industry,China's aerospace flight technology is gradually moving toward high,fine and sharp directions.In important fields such as aerospace,aluminum-lithium alloys are widely used in terms of light weight,small amount of materials,high strength and compact structure.However,the material removal rate of such parts is over 80%,using traditional The milling method not only has low processing efficiency,but also the machining accuracy of the parts and the roughness of the surface are not required.In this paper,based on the numerical control milling of aeronautical aluminum-lithium alloy thin-walled components,the multi-objective optimization of the machining process parameters is carried out to ensure the processing quality of the workpiece and improve the material removal rate under the existing processing conditions.Through the numerical control milling data of 2195 aluminum-lithium alloy thin-walled members,the influence of four variables on the surface roughness of the workpiece was analyzed by the variance method.The results show that the feed per tooth has the greatest influence on the surface roughness of the workpiece,and the others are in turn.It is the radial depth of cut,the spindle speed and the axial depth of cut,and the surface roughness increases with the increase of the feed amount per tooth.The influence of other processing parameters on the surface roughness is not obvious.Through the surface hardness test data,the work hardening of aluminum-lithium alloy thin-walled members was studied.The cutting speed and feed rate were analyzed as variables,and the work hardening law was found.In this paper,the surface roughness prediction model is established by regression method,and the accuracy of the model is verified by experiments.The milling force data obtained by the numerical control milling experiment of aluminumlithium alloy is studied and analyzed by the range method.The results show that the axial depth of cut has the greatest influence on the four machining variables,followed by the feed per tooth.The amount,radial depth of cut and axial depth of cut have little effect on it,and there is no obvious law.Based on the data obtained from orthogonal experiments,the regression prediction method was used to establish the milling force prediction model,and the accuracy of the model was verified by experiments.As a multi-objective optimization constraint condition,the cantilever beam structure was analyzed and the machining deformation was carried out by an example.Study and analyze,and use the maximum deformation amount as the constraint for multi-objective optimization.With the minimum surface roughness value and material removal rate as the optimization target,the experimental processing parameter range,the experimental milling force maximum and the machine tool performance parameters are used as constraints,and the improved particle swarm multi-objective optimization algorithm is used to optimize it.The Pareto frontier solution uses the weight coefficient calculated by the grey correlation analysis method to solve the optimal solution.The feasibility and accuracy of the optimal solution are verified by experiments.While effectively ensuring the processing quality and deformation,the processing efficiency is improved,which provides a reference for the future milling parameters selection of aluminum-lithium alloy thinwalled members.The paper has 35 pictures,26 tables,and 75 references.