Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-24T12:12:01.790Z Has data issue: false hasContentIssue false
  • English
  • Français

Lubricant homogeneity of industrial rough metallic substrates:a multivariate statistical analysis of spectroscopic ellipsometry data

Published online by Cambridge University Press:  10 May 2012

L. Abdessemed  and
M. Voué
Get access

Abstract

In this paper, a non-destructive method is proposed for measuring the density of a verythin lubricant layer (weight and spatial) on an industrial surface. We consideredspectroscopic ellipsometry measurements on rough tinplated steel substrates protected by alubrication layer. The thickness of the coating was less than the roughness parametercharacterizing the metallic surface. As the optical properties of the substrates could notbe modelled in a conventional way due to the roughness and the complex structure of themetal, the variations of one of the ellipsometric angle (Δ) were evaluated as a functionof the lubricant film surface density. After identification of the potential outliersusing a multivariate analysis technique based on the Mahalanobis distance, we interpretedthe data using the Drude’s approximation for thin dielectric films. The values of Δlinearly decrease with the lubricant surface density, allowing us to evaluate locally thelubricant surface density and its point-to-point variations.

Keywords

Spectroscopic ellipsometryoutliers detectionmultivariate analysislubricanttinplated steel (TPS)
Type
Research Article
Information
Metallurgical Research & Technology , Volume 109 , Issue 2 , 2012 , pp. 81 - 91
Copyright
© EDP Sciences 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Vignes, J.-L., André, G., Fousse, D., Bulletin de l’Union des Physiciens 88 (1994) 627-652
Rousseeuw, P.J., Hubert, M., WIREs Data Mining Knowl Discov 1 (2011) 73-79
Su, X., Tsai, C.-L., WIREs Data Mining Knowl Discov 1 (2011) 261-268
Mahalanobis, P.C., Proceedings of the National Inst. Sci. India 2 (1936) 49-55
Rousseeuw, P.J., Van Zomeren, B.C., J. Am. Stat. Assoc. 85 (1990) 633-651
Garrett, R.G., J. Geochem. Explor. 32 (1989) 319-341
Filzmoser, P., Garrett, R.G., Reimann, C., Computers & Geosciences 31 (2005) 579-587
H.G. Tompkins, E.A. Irene, Handbook of Ellipsometry, Springer, Heidelberg, 2005
H.G. Tompkins, W.A. McGahan, Spectro- scopic Ellipsometry and Reflectometry, A user’s guide, John Wiley & Sons, New-York, 1999
R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/
P. Rousseeuw, C. Croux, V. Todorov, A. Ruckstuhl, M. Salibian-Barrera, T. Verbeke, M. Maechler, robustbase: Basic Robust Statistics. R package version 0.5-0-1, 2009 http://CRAN.R-project.org/package=robustbase
M. Gschwandtner, P. Filzmoser, mvoutlier: Multivariate outlier detection based on robust methods. R package version 1.5, 2010 http://CRAN.R-project.org/package=mvoutlier
A. Gebhardt, T. Petzoldt, M. Maechler, akima: Interpolation of irregularly spaced data. R package version 0.5-4, 2009 http://CRAN.R-project.org/package=akima
Landau, L., Levich, B., Acta Physicochimica URSS 17 (1942) 42-54
V. Levich, Physicochemical Hydrodynam- ics, Prentice-Hall, Englewood Cliffs, 1962
Saxena, A.N., J. Optical Soc. Am. 55 (1965) 1061-1067