Research On Surface Morphology Of Multi Axis Ball End Milled H13 Steel And Prediction Of Friction Coefficient

The mold is called "the mother of industry",and its technological level and processing quality have become an important symbol to measure the level of a country's manufacturing industry.The cost of mold is expensive,so how to reduce cost and improve die life is the key problem in the die industry.Based on the machined surface morphology of H13 hot work die steel,the relationship between surface topography and friction and wear is studied in this thesis,providing theoretical reference for improving the service life of dies.The geometric analysis of the multi-axis ball end milling process was carried out and the relationship between the tool posture and the cutting engagement of tool tip was obtained.The reasonable tool posture was chosen to avoid the cutting engagement of tool tip,which help to improve the machining surface quality and tool life.The single-factor experiment of multi-axis ball end milling of H13 die steel was designed with the tool path and radial depth of cut being variables,and the topography of the milled surface was obtained with a white light interferometer.The characterizing parameters of the ball milling surface were determined based on the Birmingham parameter system.It was found that the radial depth of cut had a significant effect on the amplitude parameters,spatial parameters,mixing parameters,and function parameters under different tool paths.The double-tree complex wavelet transform was used to analyze the surface topography.It was found that the surface roughness of the middle frequency band was the largest;It was found that the fractal dimension has a strong resolution to the microstructure of complex shapes by fractal theory.When 1<m(the ratio of radial cutting depth to feed per tooth)was<2,the microstructure of the milling surface is the simplest;Using the autocorrelation function to analyze the topography of the milling surface,it was found that with the increase of the radial depth of cut in each tool path,the proportion of long waves in the surface topography increased.A ball-disk reciprocating friction test was conducted to obtain the coefficient of friction of each sample and the wear resistance of the sample was analyzed.It was found that under different tool paths,the friction coefficient increased at first and then decreased with the increase of radial depth of cut.The wear rate of each sample was calculated and the wear resistance of the sample was analyzed.When the friction direction was consistent with the direction of texture,the wear was dominated by adhesive wear,and the wear resistance was the worst;when the friction direction was perpendicular to the texture direction,the wear resistance was best.The wear was dominated by abrasive wear.The Pearson correlation coefficient was used to correlate the various surface morphological parameters with the friction coefficient.The root mean square deviation Sa,the average vertex curvature Ssc,the surface vertex density Sds,the maximum autocorrelation length decrease length Sal,and the fractal dimension D were found to have a strong correlation with the coefficient of friction.Based on the above conclusions,a BP neural network friction coefficient prediction model was established,and the selected training samples and test samples were repeatedly trained,and finally the prediction results were obtained.The results show that the predicted value is basically consistent with the actual measured value,and the established model was valid.