A novel robust Gaussian filtering method for the characterization of surface generation in ultra-precision machining

A lot of research work has been focused on the study of the surface generation mechanisms in order to predict the surface topography and provide the optimal machined parameters based on the experiential understanding of relationship of machined conditions and surface features. Although the formation of novel geometrical product specification (GPS) and verification framework system promotes the relevant research work to new characterization methods and draft of international standards, relative little research work was conducted on the application of surface characterization techniques to ultra-precision machining which is very important to evaluate the surface quality. In this paper, a novel robust Gaussian filtering method (RGF) is proposed and used to characterize the surface topography of ultra-precision machined surfaces. Cubic B-spline and M-estimation are used to make the method reliable and robust. Based on the property comparisons of classical weighting functions, a novel auto-developed robust weighting function (ADRF) is defined to improve the robustness of RGF. To verify the characterization feasibility of the proposed method, computer simulation is used and then the real ultra-precision machined surfaces are analyzed. The experimental results indicate that the RGF method cannot only separate the surface components effectively on the whole measured area and but also eliminates the influence of freak outliers.