A multi-scale approach of roughness measurements: Evaluation of the relevant scale

Abstract

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. AbstractThis paper proposes a new multi-scale measurement approach performed to compare the surface roughness and the visual aspect of polished surfaces. In this investigation, five specimens of glass moulds presenting different visual aspects are considered. All roughness profiles assessed by tactile profilometry were rectified by a first degree polynomial fitting, and current roughness parameters were calculated with respect to the evaluation length among which they are estimated. A variance analysis was then performed to discriminate each roughness parameter and each evaluation length with regard to a correlation with the visual aspect. Although the average roughness amplitude is about 100 nm, the results show that the optimal correlation with the visual aspect is obtained for a 400 μm evaluation length. Moreover, the multi-scale method allows to confirm results already found in the bibliography about the high wavelengths origin of "orange peel" aspect. This application allowed us to conclude on the advantages and the limits of the implemented method.

On the relation between surface roughness of metallic substrates and adhesion of human primary bone cells

“…In 3D measurements, some parameters allow to verify if surface is isotropic at the scale of the scanning length. However, this clearly means that the scale of relevance must be first known to quantify anisotropy of the surface (Van Gorp et al , ).…”

Multiscale roughness analysis of engineering surfaces: A comparison of methods for the investigation of functional correlations

“…This resampling technique estimates the coefficients' statistical modelling error in the computation to create a new dataset by randomly sampling with replacement from the original data set; then, the same statistical analysis as was carried out on the original data set is performed on the new data set [46]. Applied to the analysis of variance, this technique obtains the variability of F i;F;T ðεÞ [47].…”

Micro‐nanopatterning as tool to study the role of physicochemical properties on cell–surface interactions

“…Out of the three aforementioned roughness parameters, single roughness value is not very informative while average roughness (Ra) is; for example, it is unable to characterize lateral roughness or see the skewness of the profile, which distinguishes between peaks and valleys. 75 Skewness denoted by Rsk, is the sign that indicates whether a surface is composed mainly of pits or spikes, while kurtosis (Rku), gives an estimate of the ''peakedness'' (sharpness of the features) of a surface. Another important point is that topography parameters such as average roughness (Ra) depend on the scan length.…”