Optimization Of Milling Process And Finite Element Simulation Of Aerospace Aluminum Alloy Thin-walled Parts

Aluminum alloy thin wall parts are easy to deform in milling process.It is difficult to guarantee the machining accuracy of the work-piece after milling.As for this problem,optimization of milling parameters always plays an important role in improving the machining precision and ensuring the production efficiency.The finite element numerical simulation technology based on the modern cutting theory,the orthogonal experiment,the Matlab mathematical regression modeling ability and the optimization ability of genetic algorithm to find the optimal combination of the four elements of milling,are important means of modern mechanical processing.In this paper,we take the 7075-T651 of aerospace aluminum alloy as the research carrier to study the milling process,and design the corresponding milling experiment.We solved the milling deformation prediction model through the BP neural network algorithm and achieved the most optimal solution of the four elements of milling based on genetic algorithm.The main research contents are as follows:1?Based on theoretical model of metal cutting process,a two dimensional orthogonal cutting finite element model was established.The key technology of the simulated cutting process was analyzed in detail;Through the numerical simulation of the cutting process,the changes of stress,strain and temperature in the cutting process were obtained;2?Based on two dimensional finite element numerical simulation,a three dimensional finite element model of the milling process of aluminum alloy thin-walled parts was established.Numerical simulation of milling deformation of aluminum alloy thin wall work-piece was carried out,In the milling process of thin-walled parts,the milling force diagram and the milling deformation curve were obtained.3.The aluminum alloy thin-walled parts of the milling process was verified.By means of Kistler measuring instrument and Wenzel three coordinate measuring machine,the measuring of the milling force and the deformation of the thin wall parts after milling were carried out.The precision of milling force and milling deformation was verified by finite element numerical simulation.And through the linear regression function of Matlab,the milling force experience formula was obtained.In order to ensure the processing quality and processing efficiency,the smaller feed rate,milling depth,milling width should be chosen,and the larger milling speed should be selected.4?In order to obtain the optimization of milling parameters,BP neural network algorithm was used to train the milling parameters.The deformation prediction model of BP network processing under the multi cutting parameters was determined.The milling parameters were optimized by genetic algorithm.So better machining accuracy and higher machining efficiency of milling parameters were obtained.The parameters is Milling Rotationl Speed 3425r/min;milling speed 412mm/min;miling depth 0.65mm;milling width 2.4mm.