Outlier Elimination in Rough Surface Profilometry with Focus Variation Microscopy

Abstract

Rough surfaces such as metal additive manufactured surfaces are quite challenging for measurement. Artifacts caused by irregular and difficult-to-measure geometries are inevitable. Removing all the artifacts would cause a portion of surface information to be missing. Different from previous works, the postprocessing in this paper includes an additional step to eliminate artifacts based on autocorrelation functions of particular subimages instead of simply removing them. This increases the accuracy with respect to surface roughness and provides a more comprehensive view on the topography. In addition, a dome shape LED array ring light is proposed to provide all-round lighting due to the high degree of irregularity of workpiece surfaces. The experimental results obtained from FVM are validated and compared with the given roughness values of a Rubert Microsurf 329 comparator test panel as well as measurement results of a metal additive workpiece by a confocal microscope.