Research On The Inversion Control Of The Surface Topography Of The Splicing Area In The Milling Process Of Automobile Outer Panel Dies

The development level of the automobile industry reflects a country's overall national strength and industrial competitiveness.At the same time,China's automobile ownership is also growing rapidly,and people have higher requirements for automobile quality and aesthetics.Revealing the surface morphology of the spliced molds processed under different working conditions can effectively improve the processing accuracy of the molds,and at the same time,the multi-objective inversion control study of the milling parameters that affect the surface morphology can be used for the later processing of the surface The improvement of quality solves the difficulties in the process of automobile outer cover mold processing.This paper analyzes and experimentally studies the changes of surface morphology after milling under different working conditions with spliced samples of hardened steel of different hardness,and conducts inversion control research on the surface morphology according to the experimental data.The specific research content include:(1)Consider the vibration displacement model in the x and y directions when impact vibration exists,reconstruct the control points based on the NUBRS theory,and the surface morphology of the joint seam under the changes of feed per tooth,spindle speed and axial cutting depth Carry out simulation research.(2)Multi-scale analysis of surface topography based on wavelet packet transform,spatio-temporal transformation and energy entropy surface topography feature extraction of simulated surface topography.(3)Taking fixed-curve convex curved surface splicing dies with different hardness as the research object,the surface morphology of the samples processed under different working conditions is extracted through related machine tools and experimental equipment,and the contour signal of vibration is based on wavelet packet transform Decompose,extract the surface features of energy entropy,and verify the accuracy of the model in comparison with simulation.(4)Based on the kernel principal component analysis method,the obtained three-layer energy entropy in different frequency bands is analyzed to determine the most influential frequency band.The tool life,material removal rate and surface topography features of energy entropy are used as the objective functions,combined with the population genetic algorithm And the artificial neural network algorithm is used to inversely control the milling parameters that affect the surface shape,which provides a basis for the selection of milling parameters in the actual machining process.