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: th...

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Autores principales:	Antoine Sanner, Wolfram G. Nöhring, Luke A. Thimons, Tevis D.B. Jacobs, Lars Pastewka
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Surface roughness Autocorrelation function Spectral analysis Variable bandwidth method Tip convolution artifacts Materials of engineering and construction. Mechanics of materials TA401-492 Industrial electrochemistry TP250-261
Acceso en línea:	https://doaj.org/article/7041ad32aead4aa4a9014d124d69b16e
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spelling	oai:doaj.org-article:7041ad32aead4aa4a9014d124d69b16e2021-11-14T04:35:48ZScale-dependent roughness parameters for topography analysis2666-523910.1016/j.apsadv.2021.100190https://doaj.org/article/7041ad32aead4aa4a9014d124d69b16e2022-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666523921001367https://doaj.org/toc/2666-5239The 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.Antoine SannerWolfram G. NöhringLuke A. ThimonsTevis D.B. JacobsLars PastewkaElsevierarticleSurface roughnessAutocorrelation functionSpectral analysisVariable bandwidth methodTip convolution artifactsMaterials of engineering and construction. Mechanics of materialsTA401-492Industrial electrochemistryTP250-261ENApplied Surface Science Advances, Vol 7, Iss , Pp 100190- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Surface roughness Autocorrelation function Spectral analysis Variable bandwidth method Tip convolution artifacts Materials of engineering and construction. Mechanics of materials TA401-492 Industrial electrochemistry TP250-261
spellingShingle	Surface roughness Autocorrelation function Spectral analysis Variable bandwidth method Tip convolution artifacts Materials of engineering and construction. Mechanics of materials TA401-492 Industrial electrochemistry TP250-261 Antoine Sanner Wolfram G. Nöhring Luke A. Thimons Tevis D.B. Jacobs Lars Pastewka Scale-dependent roughness parameters for topography analysis
description	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.
format	article
author	Antoine Sanner Wolfram G. Nöhring Luke A. Thimons Tevis D.B. Jacobs Lars Pastewka
author_facet	Antoine Sanner Wolfram G. Nöhring Luke A. Thimons Tevis D.B. Jacobs Lars Pastewka
author_sort	Antoine Sanner
title	Scale-dependent roughness parameters for topography analysis
title_short	Scale-dependent roughness parameters for topography analysis
title_full	Scale-dependent roughness parameters for topography analysis
title_fullStr	Scale-dependent roughness parameters for topography analysis
title_full_unstemmed	Scale-dependent roughness parameters for topography analysis
title_sort	scale-dependent roughness parameters for topography analysis
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/7041ad32aead4aa4a9014d124d69b16e
work_keys_str_mv	AT antoinesanner scaledependentroughnessparametersfortopographyanalysis AT wolframgnohring scaledependentroughnessparametersfortopographyanalysis AT lukeathimons scaledependentroughnessparametersfortopographyanalysis AT tevisdbjacobs scaledependentroughnessparametersfortopographyanalysis AT larspastewka scaledependentroughnessparametersfortopographyanalysis
_version_	1718429903252094976

Scale-dependent roughness parameters for topography analysis

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