Optimization Of Milling Parameters Of Circular Thin-Walled Parts

Thin-walled parts are widely used in the aerospace field due to their compact and lightweight structure.In order to meet the requirements of working under extreme conditions,they often need to be processed with special materials.Titanium alloy has become an ideal raw material for the manufacture of compressor blades,bearing shells,etc.due to its advantages of good corrosion resistance,high thermal strength,and low temperature performance.However,the deformation coefficient of the titanium alloy during cutting is small,the chilling phenomenon is serious,and the cutting temperature is high,which makes its cutting difficult.In addition,when processing arc-shaped thinwalled parts,due to its poor rigidity and its special structure,it is not easy to clamp,so it is easy to produce deformation.Improper selection of processing parameters can also cause chattering during cutting,which in turn affects the surface quality of the part.Therefore,for the processing deformation and surface quality problems of arc-shaped thin-walled parts of difficult-to-machine materials,if a set of improved methods can be explored,it will have great application value to actual production.This article takes TC4 titanium alloy arc-shaped thin-walled parts as the research object,respectively from the two aspects of workpiece surface quality and machining deformation: using cutting stability lobe diagram to predict cutting vibration and improve surface quality;analysis through finite element simulation technology Workpiece processing deformation law.And the intelligent algorithm is used to optimize the cutting parameters to obtain a complete set of plans to improve the deformation and surface quality of the arc-shaped thin-walled parts.The specific research work is as follows:First,the concept of cutting chatter stability domain is introduced from the machining chatter phenomenon that affects the machining quality of the workpiece,and the dynamic model of milling of thin-walled parts is established according to the actual machining conditions of the parts.Through the method of finite element cutting simulation and hammer modal experiment,the flutter stability domain is solved,and the relationship curve between cutting parameters and cutting stability is obtained to realize the prediction of cutting vibration.The surface roughness experiment also verifies that the prediction results of the stable lobe pattern are reliable and effective.Then,according to the use characteristics of finite element dynamic cutting simulation and static cutting simulation,the local and overall machining deformation rules of the workpiece are predicted respectively,and further research is carried out on this basis,and different cutting parameters are obtained through simulation and experimental methods.Under the milling force,deformation and surface roughness data of the workpiece,analyze the influence of cutting parameters on them.Based on the experimental surface roughness data,a surface roughness prediction model was established,and its correctness was verified by residual plots and analysis of variance,which paved the way for cutting parameter optimization.Finally,the objective function and constraints are set for thin-walled parts,and a parameter optimization model is established.Use genetic algorithm combined with BP neural network to minimize the amount of processing deformation as the goal for nonlinear optimization;use the NSGA II algorithm with elite strategy to perform multiobjective optimization of surface roughness and material removal rate to obtain a series of Pareto solutions The experiment proves that the surface quality and processing efficiency of the parts processed by using the optimized cutting parameter combination are improved compared with before the optimization,which provides guidance for actual production.