Validation of Ice Roughness Analysis Based on 3D-Scanning and Self-Organizing Maps 2019-01-1992

3D-scanning is an established method for the documentation of wing ice accretion. The generated 3D-data can be used to determine specific parameters of interest, like the local ice-thickness, or the surface ice roughness. The surface roughness has significant impact on the heat transfer, and therefore on the icing process itself. Insights into the effects of surface roughness on the ice accretion and the correlated aerodynamical effects contribute to the improvement of icing codes. In this paper, the surface roughness of various test specimens is determined by performing a self-organizing maps (SOM) approach for roughness point cloud analysis on data generated with a 3D-scanner. A validation of the SOM method is achieved by means of focus variation microscopy and a mathematical proof of the utilized SOM algorithm. Different scanning systems from several manufacturers are used to determine the surface of different sandpapers. This investigation shows the limits and capabilities of state-of-the-art 3D-scanning systems in the field of surface roughness. Furthermore, the roughness model is calibrated to absolute surface parameters, such as the mean arithmetic roughness R_a or the root mean squared roughness R_q.