Experimental Study On Cutting Force And Surface Roughness Of High Speed Milling Aluminum-lithium Alloy

2060 aluminum-lithium alloy as a new material can reduce the constructional weight on the premise of ensuring the constructional strength to achieve the goal of reducing weight due to its low density, high specific strength, high specific stiffness, and good machinability. It is being applied to the key components of aircraft and it is one of the most potential materials in aerospace. However, there is less study on the machining characteristics of aluminum-lithium alloy at home and abroad, especially in the alloy machining parameter optimization. And this might affect the processing quality and efficiency of aluminum-lithium alloy milling, thus hindering the popularization and application of the material in the aerospace manufacturing field. So it is important to study the cutting performance of 2060 aluminum-lithium alloy and optimize the processing parameters to improve the machining efficiency and ensure the quality of machining.This thesis focuses on the effect laws of cutting parameters on cutting force and the machined surface roughness, optimal parameter selection through the 2060 aluminum-lithium alloy milling experiments combining with least squares support vector machine and optimization theory. The main research contents are as follows:For more systematically grasping the effect laws of cutting parameters on cutting force and surface roughness in high speed milling aluminum-lithium alloy, orthogonal experiment method was used to design 2060 aluminum-lithium alloy high speed milling orthogonal experiment with four factors and five levels. By means of range method, the influence of cutting parameters as cutting breadth, cutting depth, feed rate and rotational speed on the cutting force and surface roughness were studied. In order to further study the internal relation between cutting force, surface roughness and machining parameters, the empirical formula of cutting force and surface roughness are established by using the multiple linear regression method. Then proceeding the test of significance, the results show that the two empirical models were significant. Finally, carrying out milling experiments to verify the practical forecasting ability of the empirical models.Based on the theory of least squares support vector regression machine, more accurate modeling and prediction of surface roughness of aluminum-lithium alloy in high speed milling than the empirical models was carried out. The results of the test show that the surface roughness prediction model based on least squares support vector regression machine can accurately predict the surface roughness of 2060 aluminum-lithium alloy in high speed milling.Under the premise of ensuring the surface roughness of the machined surface, in order to improve the machining efficiency, the optimization model of cutting parameters with the goal of maximizing the material removal rate in high speed milling of aluminum-lithium alloy was established with optimization principle. The optimal parameter combination is obtained by calculating, and the effectiveness of the optimization results is verified by experiments.