Influence of noise on the fractal dimension of measured surface topography

Zuo, Xue; Peng, Minglong; Zhou, Yuankai

doi:10.1016/j.measurement.2019.107311

Cited by 16 publications

(7 citation statements)

References 31 publications

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“…This is used for the machine learning method based on the least squares support vector model. The influence of noise can be also studied on the fractal dimension of the measured surface topography, as well [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Suppression of the High-Frequency Errors in Surface Topography Measurements Based on Comparison of Various Spline Filtering Methods

Podulka

2021

Materials

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The metrology of so-called “engineering surfaces” is burdened with a substantial risk of both measurement and data analysis errors. One of the most encouraging issues is the definition of frequency-defined measurement errors. This paper proposes a new method for the suppression and reduction of high-frequency measurement errors from the surface topography data. This technique is based on comparisons of alternative types of noise detection procedures with the examination of profile (2D) or surface (3D) details for both measured and modelled surface topography data. In this paper, the results of applying various spline filters used for suppressions of measurement noise were compared with regard to several kinds of surface textures. For the purpose of the article, the influence of proposed approaches on the values of surface topography parameters (from ISO 25178 for areal and ISO 4287 for profile standards) was also performed. The effect of the distribution of some features of surface texture on the results of suppressions of high-frequency measurement noise was also closely studied. Therefore, the surface topography analysis with Power Spectral Density, Autocorrelation Function, and novel approaches based on the spline modifications or studies of the shape of an Autocorrelation Function was presented.

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Section: Introductionmentioning

confidence: 99%

Suppression of the High-Frequency Errors in Surface Topography Measurements Based on Comparison of Various Spline Filtering Methods

Podulka

2021

Materials

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“…Power spectral analysis and autocorrelation were originally developed for the analysis of time series. Contact surface profilers measure roughness along a line [3]; hence, estimating fractal dimensions by analysing data along a line is a deep-rooted tradition [61,63,70,71].…”

Section: Information Efficiencymentioning

confidence: 99%

Statistical Study of the Bias and Precision for Six Estimation Methods for the Fractal Dimension of Randomly Rough Surfaces

Flores Alarcón,

Figueroa,

Jacobo

et al. 2024

Fractal Fract

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The simulation and characterisation of randomly rough surfaces is an important topic in surface science, tribology, geo- and planetary sciences, image analysis and optics. Extensions to general random processes with two continuous variables are straightforward. Several surface generation algorithms are available, and preference for one or another method often depends on the specific scientific field. The same holds for the methods to estimate the fractal dimension D. This work analyses six algorithms for the determination of D as a function of the size of the domain, variance, and the input value for D, using surfaces generated by Fourier filtering techniques and the random midpoint displacement algorithm. Several of the methods to determine fractal dimension are needlessly complex and severely biased, whereas simple and computationally efficient methods produce better results. A fine-tuned analysis of the power spectral density is very precise and shows how the different surface generation algorithms deviate from ideal fractal behaviour. For large datasets defined on equidistant two-dimensional grids, it is clearly the most sensitive and precise method to determine fractal dimension.

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“…The measurement noise was studied with a fractal dimension and, respectively, it was found that the noise has a significant influence on the amplitude and, correspondingly, the estimated fractal dimension of low-dimensional surfaces, but has relatively less influence on the high-dimensional surfaces. Thus, denoising for low-dimensional surfaces was required prior to fractal analysis [46].…”

Section: Introductionmentioning

confidence: 99%

Proposals of Frequency-Based and Direction Methods to Reduce the Influence of Surface Topography Measurement Errors

Podulka

2022

Coatings

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Various methods, based on both surface frequency and direction, can be alternatively proposed to reduce the influence of high-frequency measurement and data analysis errors. Various types of details were studied, e.g., cylinder liners after the plateau-honing process, plateau-honed cylinder liners with additionally burnished oil pockets (dimples), turned, ground, milled or laser-textured. They were measured with stylus or non-contact (optic) techniques. It was suggested to support various frequency-based methods, e.g., Frequency Spectrum, Power Spectral Densities or Autocorrelation Function, with direction techniques to provide reduction of errors in both detection and extraction of high-frequency measurement errors. Results can be especially valuable for regular studies when frequency-based measurement errors are difficult to be identified.

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Influence of noise on the fractal dimension of measured surface topography

Cited by 16 publications

References 31 publications

Suppression of the High-Frequency Errors in Surface Topography Measurements Based on Comparison of Various Spline Filtering Methods

Suppression of the High-Frequency Errors in Surface Topography Measurements Based on Comparison of Various Spline Filtering Methods

Statistical Study of the Bias and Precision for Six Estimation Methods for the Fractal Dimension of Randomly Rough Surfaces

Proposals of Frequency-Based and Direction Methods to Reduce the Influence of Surface Topography Measurement Errors

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