An optimal discrete operator for the two-dimensional spline filter

Goto, Tomonori; Yanagi, Kazuhisa

doi:10.1088/0957-0233/20/12/125105

Cited by 15 publications

(8 citation statements)

References 6 publications

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“…For an areal and profile application, it was assumed in the ISO standards, as well [ 33 ]. In addition, a two-dimensional discrete spline filter [ 34 ] can be used to overcome this disadvantage. The edge-effect can also be reduced when high-order spline [ 35 ] approaches are used, which is a typical example of an extension of the spline filtering [ 36 ].…”

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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“…For a long time, the spline filter has lacked utility for applications requiring areal filtration, due to the areal cubic spline filter being anisotropic [9]. The newly proposed boundary conditions could be applied to the isotropic areal spline filter [10] that could find general use in 3D surface topography.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the boundary conditions of the spline filter

Tong

Zhang

Ott

et al. 2015

Meas. Sci. Technol.

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The spline filter is a standard linear profile filter recommended by ISO/TS 16610–22 (2006). The main advantage of the spline filter is that no end-effects occur as a result of the filter. The ISO standard also provides the tension parameter to make the transmission characteristic of the spline filter approximately similar to the Gaussian filter. However, when the tension parameter β is not zero, end-effects appear. To resolve this problem, we analyze 14 different combinations of boundary conditions of the spline filter and propose a set of new boundary conditions in this paper. The new boundary conditions can provide satisfactory end portions of the output form without end-effects for the spline filter while still maintaining the value of .

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“…To resolve this problem, a robust modification of the GF was proposed, receiving the RGF [26]. Splines are often applied in surface roughness evaluation [64], presenting many advantages against regular Gaussian filtering methods [65]. For the separation of the roughness, waviness, and form components of the surface topography, the regular isotropic spline filter can be proposed.…”

Section: Applied Methodsmentioning

confidence: 99%

Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors

Podulka

2022

Sensors

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Errors that occur when surface topography is measured and analysed can be classified depending on the type of surface studied. Many types of surface topographies are considered when frequency-based errors are studied. However, turned surface topography is not comprehensively studied when data processing errors caused by false estimation (definition and suppression) of selected surface features (form or noise) are analysed. In the present work, the effects of the application of various methods (regular Gaussian regression, robust Gaussian regression, and spline and fast Fourier Transform filters) for the suppression of high-frequency measurement noise from the raw measured data of turned surface topography are presented and compared. The influence and usage of commonly used available commercial software, e.g., autocorrelation function, power spectral density, and texture direction, which function on the values of areal surface topography parameters from selected (ISO 25178) standards, are also introduced. Analysed surfaces were measured with a stylus or via non-contact (optical–white light interferometry) methods. It was found that the characterisation of surface topography, based on the analysis of selected features, can be crucial in reducing measurement and data analysis errors when various filters are applied. Moreover, the application of common functions can be advantageous when feature-based studies are proposed for both profile and areal data processing.

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An optimal discrete operator for the two-dimensional spline filter

Cited by 15 publications

References 6 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

Analysis of the boundary conditions of the spline filter

Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors

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