The scale-sensitive fractal analysis (SSFA) of dental microwear textures is traditionally performed using the software Toothfrax. SSFA has been recently integrated to the software MountainsMap® as an optional module. Meanwhile, Toothfrax support has ended. Before switching to the new module, the outputs between the two software packages must be compared for consistency. We have performed such a test using Bayesian modelling on three datasets including dental surfaces of sheep (Merceron, Ramdarshan, et al., 2016) and guinea pigs (Winkler, Schulz-Kornas, Kaiser, Cuyper, et al., 2019) from controlled feeding experiments, as well as surfaces of quartzite and flint flakes used in an actualistic archeological experiment on cleaning procedures (Pedergnana, Calandra, Bob, et al., 2020). We found that the two software packages calculate significantly different values for the SSFA parameters epLsar, Asfc, HAsfc9 and R², even when the same settings are used. Nevertheless, the treatments (different diets or cleaning procedures) are discriminated similarly within each dataset. While the new software module is as good as the original software to differentiate treatments, our results imply that the outputs from the two software packages are not directly comparable and, as such, cannot be merged. Surface texture analysts should therefore consider re-analyzing published surfaces before integrating them in their studies.

Segmentation of profile features is mainly described in two standards: ISO 4287 for profile elements, used to calculate RSm and Rc , and ISO 12085 for profile motifs, used to calculate R&W parameters. These methods are commonly criticised for their unstability, although they provide valuable results when used in an adequate context [25]. A new profile segmentation based on watersheds is now introduced in ISO 21920-2 and ISO 16610-45, adapted from areal segmentation [ISO 25178-2, [22]. The areal feature parameters, chap. 3 in Characterisation of Areal Surface Texture , Leach Ed. Springer], with the hope to solve most of the existing problems. However, there are very few comparisons between these methods and almost no recommendations for the configuration of the watershed segmentation. This paper discusses the validity of applying segmentation methods on certain profiles and proposes a validity criterion. It then investigates algorithmic differences between the watershed segmentation and the classical Motifs method, compares the results between the two methods, and suggests guidelines and good practices to obtain significant correlations.

A top-down method is presented and studied for quantifying topographic map height (z) fluctuations directly from measurements on surfaces of interest. Contrary to bottom-up methods used in dimensional metrology, this method does not require knowledge of transfer functions and fluctuations of an instrument. Fluctuations are considered here to be indicative of some kinds of uncertainties. Multiple (n), successive topographic measurements (z = z(x,y)) are made at one location without moving the measurand relative to the measurement instrument. The measured heights (z) at each position (x,y) are analyzed statistically. Fluctuation maps are generated from the calculated variances. Three surfaces were measured with two interferometric measuring microscopes (Bruker ContourGT™ and Zygo NewView™ 7300). These surfaces included an anisotropic, turned surface; an isotropic, sandblasted surface; and an abraded, heterogeneous, multilayer surface having different, complex, multiscale morphologies. In demonstrating the method, it was found that few non-measured points persisted for all 100 measurements at any location. The distributions of uncertainties are similar to those of certain features on topographic maps at the same locations, suggesting that topographic features can augment measurement fluctuations. This was especially observed on the abraded ophthalmic lens; a scratch divides the topographic map into two zones with different uncertainty values. The distributions of fluctuations can be non-Gaussian. Additionally, they can vary between regions within some measurements.