Can I read Scale-dependent Roughness Parameters for Topography Analysis on EtoBox?

Scale-dependent Roughness Parameters for Topography Analysis by Sanner, Antoine; Nöhring, Wolfram G.; Thimons, Luke A.; Jacobs, Tevis D. B.; Pastewka, Lars is a scholarly article available to read on EtoBox.

What is Scale-dependent Roughness Parameters for Topography Analysis about?

The failure of roughness parameters to predict surface properties stems from their inherent scale-dependence; in other words, the measured value depends on the way it was measured. Here we take advantage of this scale-dependence to develop a new framework for characterizing rough surfaces: the Scale-Dependent Roughness Parameters (SDRP) analysis that yields slope, curvature and higher-order derivatives of surface topography at many scales, even on a single topography measurement. We demonstrate the relationship between SDRP and other common statistical methods for analyzing surfaces: the height-difference autocorrelation function (ACF), variable bandwidth methods (VBMs) and the power spectral density (PSD). We use computer-generated and measured topographies to demonstrate the benefits of SDRP analysis, including: novel metrics for characterizing surfaces across scales, and the detection of measurement artifacts. The SDRP is a generalized framework for scale-dependent analysis of surface topography that yields metrics that are intuitively understandable.

Author

Sanner, Antoine; Nöhring, Wolfram G.; Thimons, Luke A.; Jacobs, Tevis D. B.; Pastewka, Lars

Published

2021

Language

EN