Simulation Of Machining Deformation And Optimization Of Cutting Parameters Research For Large-scale Rotary Thin-walled Parts

With the constantly updated design concept of aviation products,in order to reduce weight and improve structural strength,product design has generally shifted to the design of large-scale and complex overall structures.Therefore,a large number of large-scale thin-walled structural parts have been emerged.At the same time,aviation weaponry has become increasingly high-tech,intelligent,and integrated,and the requirements of parts machining accuracy have also increased.However,large thinwalled parts have the characteristics of complicated structure,large size,and poor rigidity,which make them easily deformed during processing,resulting in the parts fail to meet the machining accuracy requirements.Therefore,for the turning machining of large-scale rotary thin-walled structural parts,research on finite element simulation of machining deformation and optimization of cutting parameters can be applied universally to aviation equipment manufacturing projects,which will help improve the quality,performance,and processing efficiency of China's aviation thin-wall revolving parts,and has a broad application prospect.Based on the finite element analysis,this paper predicts the machining deformation of large-scale thin-walled parts.The combination of artificial intelligence and optimization techniques is used to achieve optimization of multi-objective cutting parameters.At the same time,process experiments are performed to correct and verify.An effective control strategy for the machining deformation of typical rotary thinwalled parts is established,which guarantees the machining precision requirements and production efficiency of thin-walled parts in the same time.The specific research work is as follows:Firstly,using ABAQUS finite element simulation software,a three-dimensional coupled thermo-mechanical finite element model for aviation aluminum alloy 7050-T7451 was constructed to simulate the machining process.The simulation results of cutting force,temperature field,stress and strain field during the cutting process were analyzed.The basic rule of the influence of cutting parameters on cutting force was studied.The cutting force experiment showed that the simulation results were accurate and reliable.Secondly,according to the research object of this paper,using finite element dynamic cutting simulation and static cutting simulation technology,the finite element model was constructed to simulate the local and overall machining deformation of the workpiece,and the machining deformation experiment was conducted to verify the simulation results.Through discussion and analysis,it is concluded that considering the limitations of dynamic cutting simulation at the present stage that are difficult to solve,this paper uses the finite element static cutting simulation method to predict the maximum machining deformation of large-scale rotary thin-walled parts under different cutting parameters.Finally,based on the maximum deformation data sample obtained by simulation,the nonlinear mapping relationship between cutting parameters and turning deformation of thin-walled parts was established based on BP neural network.With machining deformation and production efficiency as objective functions,the optimization of cutting parameters was accomplished by using genetic algorithms.Comparing the simulation to the experiment,the results show that the optimized cutting parameters can meet the high machining accuracy requirements and production efficiency of the thin-walled parts,which have high practicability and can be used to guide production.