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Title: Scale-dependent roughness parameters for topography analysis
The failure of roughness parameters to predict surface properties stems from their inherent scale-dependence; in other words, the measured value depends on how the parameter 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, which yields slope, curvature, and higher-order derivatives of surface topography at many scales, even for 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.  more » « less
Award ID(s):
1727378 1844739
NSF-PAR ID:
10310034
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Applied surface science advances
Volume:
7
Issue:
2022
ISSN:
2666-5239
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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