% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@ARTICLE{Brinckmann:891753,
author = {Brinckmann, Steffen and Schwaiger, Ruth},
title = {{T}owards enhanced nanoindentation by image recognition},
journal = {Journal of materials research},
volume = {36},
issn = {2044-5326},
address = {Cambridge [u.a.]},
publisher = {Cambridge Univ. Press},
reportid = {FZJ-2021-01713},
pages = {2266-2276},
year = {2021},
abstract = {The Oliver–Pharr method is maybe the most established
method to determine a material’s Young’s modulus and
hardness. However, this method has a number of requirements
that render it more challenging for hard and stiff
materials. Contact area and frame stiffness have to be
calibrated for every tip, and the surface contact has to be
accurately identified. The frame stiffness calibration is
particularly prone to inaccuracies since it is easily
affected, e.g., by sample mounting. In this study, we
introduce a method to identify Young’s modulus and
hardness from nanoindentation without separate area function
and frame stiffness calibrations and without surface contact
identification. To this end, we employ automatic image
recognition to determine the contact area that might be less
than a square micrometer. We introduce the method and
compare the results to those of the Oliver–Pharr method.
Our approach will be demonstrated and evaluated for
nanoindentation of Si, a hard and stiff material, which is
challenging for the proposed method.},
cin = {IEK-2},
ddc = {670},
cid = {I:(DE-Juel1)IEK-2-20101013},
pnm = {122 - Elektrochemische Energiespeicherung (POF4-122) / 1241
- Gas turbines (POF4-124)},
pid = {G:(DE-HGF)POF4-122 / G:(DE-HGF)POF4-1241},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000636935300001},
doi = {10.1557/s43578-021-00173-x},
url = {https://juser.fz-juelich.de/record/891753},
}