Study On Tool Angels-based Control Technology Of Milling Deformation For Thin-walled Workpiece

Among the aerospace industrial products, thin-walled parts are used commonlybecause of its excellent characteristics. These thin-walled parts have complexstructure, high precision, and also because of the weak rigidity, the cutting force in thecutting prone machining deformation, resulting in thick under the thin wall thicknessand size tolerance, it is difficult to control the workpiece machining accuracy, and themachined parts may not be up to the quality indicators. Therefore, it is of greatsignificance to effectively control the processing of the workpiece deformation inaviation industry.During the operation of thin-walled workpiece, the tool parameter has asignificant impact on the size of the cutting force, milling thermal deformation andsurface microscopic quality. The deformation law of workpiece which is caused bysingle tool angle can be obtained by finite element method, and the deformation lawof workpiece caused by multiple tool angles can be predicted by the method ofartificial neural network. In this paper, the cutter angle milling distortion ofthin-walled parts are studied by finite element technology, combining with moderncutting theory, artificial neural networks, mathematical modeling and other methods.We also use genetic algorithm to optimize the milling angle. The main research asfollows:1.Analysis of the elastic-plastic model which based on the thermal coupling.Milling finite element simulation, first we need a powerful numerical softwareABAQUS, also need to study the key technologies in the simulation, and then it willreflect the actual process.2.Use three-dimensional modeling software CATIA to make a cutter model, thenimport it to ABAQUS finite element software to establish a three-dimensionalsimulation model of the thin-walled parts processing. And use this model to simulatethe milling process of7050-T7451aviation aluminum alloy thin-walled parts which isbased on thermal coupling. The comparison of the simulated values with theexperimental results is carried out to validate the proposed three-dimensional cuttermodel. 3.We study on BP neural network,based on the Finite element simulation results.the Finite element simulation results dates are used as the training samples of neural network to determine various parameters of the neural network.Thus,we obtain the prediction model of processing deformation on account of the BP neural network of milling cutter angle.the complex relationship between the combination of different angles and processing deformation can be predicted,the evaluation and design of cutter anglecan be achieved effectively. It can improve the machining accuracy,shorten the developing and manufacturing period,and reduced the manufacturing cost.4.The Cutter orthogonal rakes and helix angle are optimized by the Geneticalgorithms. Take advantage of Genetic algorithms to search for the optimal solution inthe global scope，and determine various parameters of the neural network bycombined the model for milling of thin-walled workpiece with the simulateresult.Then,we get the optimal combination of the Cutter orthogonal rakes and Helixangle with BP neural network above.We carried on simulation experiments with theOptimization results and make an analysis of the experiments results. The resultsdemonstrate the effectiveness of the Optimization results.