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The surface-topography challenge : a multi-laboratory benchmark study to advance the characterization of topography
ID
Pradhan, A.
(
Author
),
ID
Müser, M. H.
(
Author
),
ID
Miller, N.
(
Author
),
ID
Abdelnabe, J. P.
(
Author
),
ID
Afferrante, L.
(
Author
),
ID
Albertini, D.
(
Author
),
ID
Aldave, D. A.
(
Author
),
ID
Algieri, L.
(
Author
),
ID
Ali, N.
(
Author
),
ID
Almqvist, A.
(
Author
),
ID
Kalin, Mitjan
(
Author
),
ID
Polajnar, Marko
(
Author
),
ID
Požar, Tomaž
(
Author
),
ID
Samodurova, Anastasia
(
Author
)
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https://link.springer.com/article/10.1007/s11249-025-02014-y
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Abstract
Surface performance is critically influenced by topography in virtually all real-world applications. The current standard practice is to describe topography using one of a few industry-standard parameters. The most commonly reported number is a, the average absolute deviation of the height from the mean line (at some, not necessarily known or specified, lateral length scale). However, other parameters, particularly those that are scale-dependent, influence surface and interfacial properties; for example the local surface slope is critical for visual appearance, friction, and wear. The present Surface-Topography Challenge was launched to raise awareness for the need of a multi-scale description, but also to assess the reliability of different metrology techniques. In the resulting international collaborative effort, 153 scientists and engineers from 64 research groups and companies across 20 countries characterized statistically equivalent samples from two different surfaces: a “rough” and a “smooth” surface. The results of the 2088 measurements constitute the most comprehensive surface description ever compiled. We find wide disagreement across measurements and techniques when the lateral scale of the measurement is ignored. Consensus is established through scale-dependent parameters while removing data that violates an established resolution criterion and deviates from the majority measurements at each length scale. Our findings suggest best practices for characterizing and specifying topography. The public release of the accumulated data and presented analyses enables global reuse for further scientific investigation and benchmarking.
Language:
English
Keywords:
surface topography
,
roughness metrics
,
multi-scale topography
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
FS - Faculty of Mechanical Engineering
Publication status:
Published
Publication version:
Version of Record
Year:
2025
Number of pages:
26 str.
Numbering:
Vol. 73, art. 110
PID:
20.500.12556/RUL-171045
UDC:
539.92
ISSN on article:
1023-8883
DOI:
10.1007/s11249-025-02014-y
COBISS.SI-ID:
244191491
Publication date in RUL:
29.07.2025
Views:
697
Downloads:
295
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Record is a part of a journal
Title:
Tribology letters
Shortened title:
Tribol. lett.
Publisher:
Springer Nature
ISSN:
1023-8883
COBISS.SI-ID:
2806555
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Projects
Funder:
NSF - National Science Foundation
Project number:
1844739
Name:
CAREER: Understanding Nanoparticle Adhesion to Guide the Surface Engineering of Supporting Structures
Funder:
NSF - National Science Foundation
Project number:
CMMI-2400999
Funder:
Other - Other funder or multiple funders
Funding programme:
Deutsche Forschungsgemeinschaft
Project number:
EXC-2193/1-390951807
Funder:
Other - Other funder or multiple funders
Funding programme:
European Research Council
Project number:
StG 747343
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