Characterisation of Surface Roughness for Ultra-Precision Freeform Surfaces

Ultra-precision freeform surfaces are widely used in many advanced optics applications which demand for having surface roughness down to nanometer range. Although a lot of research work has been reported on the study of surface generation, reconstruction and surface characterization such as MOTIF and fractal analysis, most of them are focused on axial symmetric surfaces such as aspheric surfaces. Relative little research work has been found in the characterization of surface roughness in ultra-precision freeform surfaces. In this paper, a novel Robust Gaussian Filtering (RGF) method is proposed for the characterisation of surface roughness for ultra-precision freeform surfaces with known mathematic model or a clould of discrete points. A series of computer simulation and measurement experiments were conducted to verify the capability of the proposed method. The experimental results were found to agree well with the theoretical results.Department of Industrial and Systems Engineerin

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.Department of Industrial and Systems Engineerin

Characterisation of surface roughness for ultra-precision freeform surfaces

Ultra-precision freeform surfaces are widely used in many advanced optics applications which demand for having surface roughness down to nanometer range. Although a lot of research work has been reported on the study of surface generation, reconstruction and surface characterization such as MOTIF and fractal analysis, most of them are focused on axial symmetric surfaces such as aspheric surfaces. Relative little research work has been found in the characterization of surface roughness in ultra-precision freeform surfaces. In this paper, a novel Robust Gaussian Filtering (RGF) method is proposed for the characterisation of surface roughness for ultra-precision freeform surfaces with known mathematic model or a clould of discrete points. A series of computer simulation and measurement experiments were conducted to verify the capability of the proposed method. The experimental results were found to agree well with the theoretical results

Characterisation of surface roughness for ultra-precision freeform surfaces

Ultra-precision freeform surfaces are widely used in many advanced optics applications which demand for having surface roughness down to nanometer range. Although a lot of research work has been reported on the study of surface generation, reconstruction and surface characterization such as MOTIF and fractal analysis, most of them are focused on axial symmetric surfaces such as aspheric surfaces. Relative little research work has been found in the characterization of surface roughness in ultra-precision freeform surfaces. In this paper, a novel Robust Gaussian Filtering (RGF) method is proposed for the characterisation of surface roughness for ultra-precision freeform surfaces with known mathematic model or a clould of discrete points. A series of computer simulation and measurement experiments were conducted to verify the capability of the proposed method. The experimental results were found to agree well with the theoretical results

Characterisation of surface roughness for ultra-precision freeform surfaces

Ultra-precision freeform surfaces are widely used in many advanced optics applications which demand for having surface roughness down to nanometer range. Although a lot of research work has been reported on the study of surface generation, reconstruction and surface characterization such as MOTIF and fractal analysis, most of them are focused on axial symmetric surfaces such as aspheric surfaces. Relative little research work has been found in the characterization of surface roughness in ultra-precision freeform surfaces. In this paper, a novel Robust Gaussian Filtering (RGF) method is proposed for the characterisation of surface roughness for ultra-precision freeform surfaces with known mathematic model or a clould of discrete points. A series of computer simulation and measurement experiments were conducted to verify the capability of the proposed method. The experimental results were found to agree well with the theoretical results