The Research Of Surface Roughness Of High Speed Cutting Aluminum Alloy

As an important branch of advanced manufacturing technology,high-speed cutting technology plays an important role in many fields,such as defense,aerospace,automotive engine manufacturing and so on.High-speed cutting can effectively improve production efficiency,significantly improve the machining accuracy and quality of parts,for the development of manufacturing enterprises and the national economy has improved a strong guarantee.However,due to the late start of high-speed cutting technology in China,the empirical method is still used in the production process.There is no mature solution for reference,so it is urgent to research and theory for guiding production.Firstly,the high speed cutting technology is introduced,and the chip characteristics of several typical metal materials are analyzed according to the high speed cutting theory.The research shows that the degree of sawtooth increases with the increase of cutting speed,and the general rule of cutting deformation is discussed.Finally,several common aluminum alloys are introduced.The effect of cutting speed on the surface roughness of aluminum alloy was analyzed.The results show that the surface roughness of aluminium alloy under high speed cutting condition is less than that under ordinary cutting condition.It is basically verified that high speed cutting can effectively improve the surface quality of parts.In the further research,based on the school-enterprise cooperation project jointly undertaken by Kunming University of Technology and Kunming Machine Tool Co.,Ltd.,Shenyang Machine Tool Group,a high-precision horizontal coordinate boring machine(model TGK46100)is used as the experimental platform to milling and process the aluminum alloy material(ZL114A)for the cylinder head of a diesel engine.The experimental design and range analysis are carried out.The influence of cutting parameters(milling speed,feed per tooth and back feed)on the surface roughness of parts is studied.The results show that,under the condition of high speed cutting,if other parameters are the same,the influence of feed per tooth on the roughness is greater than that of milling speed on the roughness.Based on the analysis of variance and significance level,it is concluded that the feed per tooth has a highly significant impact on the roughness,while the milling speed has a significant impact on the roughness,and the back feed has no significant impact on the roughness.Finally,according to the principle of maximizing the signal-to-noise ratio,the cutting parameters selected in the actual production process are optimized and predicted.On the basis of the above analysis,the regression equation is initially established by regression analysis and the results of Alauddin et al.The regression coefficients are solved by the least square method based on the experimental data.The prediction models of surface roughness and cutting parameters are obtained,and the roughness values are predicted according to the models.With the help of BP neural network tool,the roughness data are predicted based on the experimental data.Two different prediction methods were used to calculate the predicted value,and compared with the measured real value to verify the advantages and disadvantages of the two methods.The results show that the predicted results of the BP neural network model for surface roughness of parts are better than those of the regression analysis method.