Comparison and validation of surface topography segmentation methods for feature-based characterisation of metal powder bed fusion surfaces

Feature-based characterisation, i.e. the characterisation of surface topography based on the isolation of relevant topographic formations (features) and their dimensional assessment, is a developing field of surface texture metrology. Feature-based approaches provide dimensional assessments of individual features (area, width, height, etc) as well as statistical properties of feature aggregations (e.g. mean, standard deviation, etc), which may be more intuitive or related to functionality. For powder bed fusion surfaces, a commonly investigated feature of interest is the particles or spatter present on the surface. In this work, we address segmentation, a necessary step of feature-based characterisation, where the measured surface topography is spatially partitioned into regions to isolate the targeted features from their surroundings. Three topography segmentation methods are investigated: morphological segmentation on edges, contour stability analysis and active contours. To perform the comparison, three powder bed fusion surfaces obtained at differing build orientations (0;, 30; and 90;) and measured using focus variation microscopy are subjected to the three segmentation approaches - optimised to isolate spatter and particles on the surface. The comparison of the segmentation methods focuses on performance in feature identification (i.e. the capability to correctly detect the presence of features) and performance in feature boundary determination (i.e. the capability to correctly trace the boundaries of each feature). Results show that no segmentation method is consistently superior for all test cases, but the comparison approach is useful to explore and optimise segmentation alternatives for feature-based characterisation scenarios.