Scale-dependent roughness parameters for topography analysis
نویسندگان
چکیده
The failure of roughness parameters to predict surface properties stems from their inherent scale-dependence; in other words, the measured value depends on how parameter was measured. Here we take advantage this scale-dependence develop a new framework for characterizing rough surfaces: Scale-Dependent Roughness Parameters (SDRP) analysis, which yields slope, curvature, and higher-order derivatives topography at many scales, even single measurement. We demonstrate relationship between SDRP common statistical methods analyzing height-difference autocorrelation function (ACF), variable bandwidth (VBMs) power spectral density (PSD). use computer-generated topographies benefits including: novel metrics surfaces across detection measurement artifacts. is generalized scale-dependent analysis that are intuitively understandable.
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ژورنال
عنوان ژورنال: Applied surface science advances
سال: 2022
ISSN: ['2666-5239']
DOI: https://doi.org/10.1016/j.apsadv.2021.100190