Exact Counting of Random Height Features of Product Surfaces

Abstract

Although many statistical parameters are readily derivable from the autocorrelation function, relevant computations make their acquisition infeasible if required for product surface roughness where such a function can only be expressed in its digital form. Presented is a semianalytical approach that significantly reduces numerical computations conventionally followed to obtain width-type statistics of surface topography from the height autocorrelation function (HACF). The approach is based on fitting the digital form of the HACF to an analytic damped cosine that can then be readily differentiated and integrated. The applicability and accuracy of the proposed approach are illustrated for sampled height data experimentally collected from real product surfaces. Comparison of results using both conventional and suggested approaches shows that analytic fitting of the HACF leads to a rich set of descriptive width-type statistics as accurate as but less time consuming than conventional numerical techniques.