Generation and Reproduction of Random Rough Surfaces

In both mixed lubrication and dry contact studies, statistically meaningful conclusions often require a large set of numerical rough surfaces. While such surfaces can be acquired through metrological tools such as interferometers, they rarely exhibit the exact height or spatial parameters of interest, and the available datasets are typically limited. Although the numerical generation of rough surfaces is not a new subject, its relevance has grown, and important challenges remain. Building on our earlier work, in which a new generation method was introduced, the authors extend its scope to produce surfaces with prescribed height and spatial parameters, under non-periodicity constraints and arbitrary orientation anisotropy. In addition, we propose the reproduction of existing rough surfaces for future AI training applications and highlight topographic patterning as the next major challenge to address.