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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-02-01
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| Series: | Applied Surface Science Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666523921001367 |
| _version_ | 1818823853700284416 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-18T23:46:35Z |
| format | Article |
| id | doaj.art-7041ad32aead4aa4a9014d124d69b16e |
| institution | Directory Open Access Journal |
| issn | 2666-5239 |
| language | English |
| last_indexed | 2024-12-18T23:46:35Z |
| publishDate | 2022-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Surface Science Advances |
| spelling | doaj.art-7041ad32aead4aa4a9014d124d69b16e2022-12-21T20:47:11ZengElsevierApplied Surface Science Advances2666-52392022-02-017100190Scale-dependent roughness parameters for topography analysisAntoine Sanner0Wolfram G. Nöhring1Luke A. Thimons2Tevis D.B. Jacobs3Lars Pastewka4Department of Microsystems Engineering, University of Freiburg, Georges-Köhler-Allee 103, Freiburg 79110, Germany; Cluster of Excellence livMatS, Freiburg Center for Interactive Materials and Bioinspired Technologies, University of Freiburg, Georges-Köhler-Allee 105, Freiburg 79110, GermanyDepartment of Microsystems Engineering, University of Freiburg, Georges-Köhler-Allee 103, Freiburg 79110, GermanyDepartment of Mechanical Engineering and Materials Science, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, Pennsylvania 15261, USADepartment of Mechanical Engineering and Materials Science, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, Pennsylvania 15261, USACorresponding author.; Department of Microsystems Engineering, University of Freiburg, Georges-Köhler-Allee 103, Freiburg 79110, Germany; Cluster of Excellence livMatS, Freiburg Center for Interactive Materials and Bioinspired Technologies, University of Freiburg, Georges-Köhler-Allee 105, Freiburg 79110, GermanyThe 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.http://www.sciencedirect.com/science/article/pii/S2666523921001367Surface roughnessAutocorrelation functionSpectral analysisVariable bandwidth methodTip convolution artifacts |
| spellingShingle | Antoine Sanner Wolfram G. Nöhring Luke A. Thimons Tevis D.B. Jacobs Lars Pastewka Scale-dependent roughness parameters for topography analysis Applied Surface Science Advances Surface roughness Autocorrelation function Spectral analysis Variable bandwidth method Tip convolution artifacts |
| title | 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_short | Scale-dependent roughness parameters for topography analysis |
| title_sort | scale dependent roughness parameters for topography analysis |
| topic | Surface roughness Autocorrelation function Spectral analysis Variable bandwidth method Tip convolution artifacts |
| url | http://www.sciencedirect.com/science/article/pii/S2666523921001367 |
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