Optimization And Simulation Of Milling Process Parameters Of Aluminum Alloy Thin-walled Parts

Aluminum alloy thin-walled parts have the characteristics of light weight,high specific strength,good load-bearing performance,and compact structure.Therefore,they are widely used in automobiles,molds and other industries.However,its rigidity is low and its thickness is small.In the process of milling,aluminum alloy thin-walled parts are extremely prone to deformation.As a result,the machining accuracy,surface quality,and machining efficiency of the parts cannot be effectively guaranteed,and sometimes the parts are directly scrapped.The selection of milling process parameters is one of the main factors affecting the machining performance of thin-walled parts.In traditional NC machining processes,relying on consulting the machining manual and selecting process parameters based on machining experience often do not take into account the impact of machining deformation on machining accuracy.Suboptimal results.At present,although there have been studies on the optimization of process parameters,most of them focus on single-objective optimization,or convert multiobjective optimization problems into single-objective optimization problems to solve,and the optimization results are often based on reducing performance in many other aspects.Based on the indicators,real multi-objective optimization cannot be achieved.Therefore,research on multi-objective optimization of milling process parameters of thin-walled aluminum alloy parts is of great significance to improve the processing performance and processing efficiency of thin-walled aluminum alloy parts,reduce manufacturing costs,increase equipment life,and realize green manufacturing,and provide a theory for production practice guide.Aiming at the above problems,this article takes material removal rate,surface roughness and maximum machining deformation as optimization targets,and conducts indepth research on the optimization of process parameters during the milling process of6061 aluminum alloy thin-walled parts.The surface roughness and material removal rate data of the actual milling process were obtained through the orthogonal test of milling,and the deformation data of the milling process were obtained using the finite element simulation technology.The genetic algorithm(GA),particle swarm algorithm(PSO),error back propagation neural network(BP),and support vector machine(SVM)were used to establish the GA-BP surface roughness prediction model and the PSO-SVM milling deformation prediction model.A multi-objective optimization model was established using optimization techniques,and the multi-objective optimization model was solved by the fast non-dominated genetic algorithm(NSGA-II),and the recommended optimization parameters were given.Finally,the system software for milling process parameter optimization was developed.The specific research contents and results of this article are as follows:(1)Taking the milling process of 6061 aluminum alloy thin-walled parts as the research object,and using the tool diameter,axial feed,radial feed,feed speed,and spindle speed as test variables,use orthogonal tests to obtain thin-walled parts Related data of surface roughness and material removal rate during milling,and the experimental data of surface roughness and material removal rate were analyzed using analysis of variance and range analysis method,and various processing parameters were studied for material removal rate and surface roughness,respectively.Degree of influence degree and influence law.(2)Based on the results of orthogonal experiments,four prediction models for predicting surface roughness were established based on BP neural network,support vector machine,genetic algorithm and particle swarm optimization algorithm.BP neural network prediction model,SVM prediction model,GA-BP prediction model and PSO-SVM prediction model.Through experimental verification,the GA-BP prediction model and PSO-SVM prediction model have high prediction accuracy and small error,and are suitable for surface roughness prediction.(3)Based on the metal cutting principle,elastoplastic mechanics,and finite element simulation technology,the milling process of 6061 aluminum alloy thin-walled parts was studied,the influence of milling process parameters on the processing deformation was analyzed,and GA-BP machining deformation prediction model.(4)Using the NSGA-II algorithm,the surface roughness,material removal rate,and machining deformation of 6061 aluminum alloy thin-walled parts are optimized as the optimization goals.The objective functions of the three are established.The machine tool power,machine parameters,and milling processing As the constraint conditions,the parameters of spindle speed,tool diameter,radial feed,axial feed,and feed speed were analyzed for parameter optimization,and the most suitable milling parameters under the condition of the objective function were found.Excellent combination.(5)The overall design of the milling process parameter optimization system,database structure design and database system function design are realized.Based on MFC,My SQL and MATLAB,an object-oriented programming method was used to develop the milling process parameter optimization system software.