Kinematic and stochastic surface topography of workpiece made of Al7075 in flank milling

Abstract

In this paper, a method is given to predict the surface topography in flank milling based on the experiment data of Al7075. First, the kinematic topography of the machined surface is analyzed with dexel model by CutS. Then, the measured stochastic topography is described by four-distribution-moment Gaussian distribution and quadratic regression is used to fit the values. Abbott curve is applied in pseudo random number generation to create the simulated stochastic topography. Finally, the combination of the kinematic and stochastic topography is calculated by material removal simulations (MRS) and is used to predict the real measured topography. The results show that the roughness of the stochastic topography increases with an increased feed per tooth and depth of cut, and the combined topography mostly matches to the measured one within the proposed method.