Characterization and functional evaluation of surface texture of micro eccentric shaft based on multi-index

Abstract

Micro eccentric shaft has important application in many high-tech fields because of its small specific gravity, material, and energy saving. The machined surface texture has an indispensable influence on the surface integrity and the final functional capability. However, due to the micro scale and weak rigidity, it is difficult to characterize the surface texture and evaluate the functionality by traditional quantitative parameters. In order to comprehensively realize the surface texture characterization and functional analysis, a mathematical model is established to simulate the surface texture machined with different cutting tools. Then the surface microscopic profile and functional performance of the surface texture are analyzed by amplitude distribution function (ADF) and bearing area curve (BAC), and the surface texture is also evaluated by fractal dimension, which can avoid the negative effects of scale and resolution. Furthermore, power spectrum density (PSD) is utilized to analyze the relationship between the process dynamic state and geometrical specification of the surface texture. The validity of experimental results shown that the microscopic height distribution of surface machined by flat end milling cutter tends to be more random and there are more microscopic geometric features than that of the ball end milling cutter. The machined surface obtained by the flat end milling cutter has better load bearing, wear resistance, and liquid retention capability.