How to Select 2D and 3D Roughness Parameters at Their Relevant Scales by the Analysis of Covariance

Tchoundjeu, Stephane; Bigerelle, Maxence; Robbe-Valloire, François; Botelho, Tony da Silva; Jarnias, Frédéric

doi:10.3390/ma13071526

Cited by 4 publications

(5 citation statements)

References 55 publications

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“…If a characterization parameter is unable to see differences between clouds, F values will be close to unity. For more details, see [69] for the formulation, [70] for application in wear, [71] in lubrification, [72] in biology and [73] for coating functionalities. For each roughness parameter at a given scale, F value is computed.…”

Section: Relevance Of the Topographic Characterization Parametersmentioning

confidence: 99%

Fractal and statistical characterization of brushstroke on paintings

Bigerelle

Guibert

Mironova

et al. 2023

Surf. Topogr.: Metrol. Prop.

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Identification of an individual artist’s touch on paintings is studied using surface metrology. Paintings’ topographies were measured using focus variation and stitching, creating 13 x 13 mm maps with 1 µm sampling intervals, and 169 megapixels, with a 10X objective lens. Topographic characterization parameters were analyzed for their ability to differentiate different painters’ renderings. Statistical treatments from data mining were used to discriminate, by optimization, multiscale topographic signatures characterized by a multitude of areal texture parameters. It appears that a fractal dimension can define 3 characteristic scale ranges. One from 3 to 70 µm corresponds to brushstroke details. Another, from 70 to 700 µm, corresponds to the topography of the material of the canvas fabric. Finally, scales greater than 700 µm correspond to undulations of the canvas. For scales less than 50 µm, the fractal structure of the topography left by brushstrokes follows a power law characterized by the slopes of the topography. The topography of the clouds painted on the canvas has an Sdq (topographic slopes) increasing with the clarity of the clouds at scales of 3-500 µm. According to the Torrance-Sparrow theory, the higher the Sdq, the more diffuse the light on the surface. The painter therefore wanted to show, by his brushstroke, that the light clouds diffuse more light giving an impression of local brightness. This study is confirmed by the analysis of the painting of Max Savy, a French painter from Carcassonne (1918-2009), which was measured with a white light interferometer Zygo NewView 7300, a X100 objective lens giving a 517 µm x 517 µm stitched surface, with a sampling interval of 0.109 µm. The box-counting method for estimating the fractal dimension of the topography of an oil painting appears optimal by the fact that it morphologically integrates scale variations of the local slopes of the surface morphology. This method thus characterizes the multiscale aspects, as well as the scale changes, of the topography.

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Section: Relevance Of the Topographic Characterization Parametersmentioning

confidence: 99%

Fractal and statistical characterization of brushstroke on paintings

Bigerelle

Guibert

Mironova

et al. 2023

Surf. Topogr.: Metrol. Prop.

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“…When correlations between categorical as well as continuous variables and surface topography parameters have to be evaluated, a multiple linear regression method can be used. For example, Tchoundjeu et al (2020) [21] determined the relevance of the roughness parameters using the F-value, which is a measure of the overall significance of the regression model. However, this method is only applicable if at least one of the variables of interest is a continuous variable.…”

Section: Statistical Methods For Roughness Parameter Selectionmentioning

confidence: 99%

“…Different statistical methods have been used in the selection of the most relevant roughness parameters for the description of machined surfaces. These methods include variation analysis [15], correlation analysis [16][17][18], regression [19][20][21], analysis of variance (ANOVA) [22][23][24][25] and classification algorithms [26].…”

Section: Introductionmentioning

confidence: 99%

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Systematic selection and evaluation of relevant surface roughness parameters for the characterisation of the innovative RPM-Synchronous Grinding process

Newrkla

Spenger²,

Cihak-Bayr

2021

Surf. Topogr.: Metrol. Prop.

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The key to high performances and long lifetimes of machine components in lubricated contacts is often the surface topography. Its characteristics can be derived in numerous ways from 3D topography measurements, but these procedures do not follow any existing standards, resulting in limited comparability. In this work, we present a new, universally applicable workflow to reveal the most significant roughness parameters for a comprehensive description of differences in surface topographies. This workflow, based on principal component analysis (PCA), offers a standardization of parameter selection. It is applied to RPM-Synchronous Grinding (RSG), a novel grinding process that enables the production of non-circular geometries without an oscillating movement. To increase trust in this new technique, knowledge on how the process parameters affect the surface topography is required. Numerous statistical roughness parameters were derived from 3D confocal light microscopy as well as 2D tactile measurements on each ground workpiece. We apply the proposed workflow and find that, for the selected RSG parameter variations, Rq is the most relevant roughness parameter to capture changes in the surface topography. The 100 % fused white aluminum oxide grinding wheel, opposite grinding direction, and a low specific material removal rate result in the smoothest surfaces. A high degree of usage of the grinding wheel leads to higher Rq values, but there is a trend to reach a plateau value. The RSG machined workpieces are compared to conventionally ground and shortly run camshafts. The camshafts have Rq values in the range of the rougher RSG machined surfaces, but there are significant differences in the parameters Ssk, Sku, Sv and Vvv. Provided the number of workpieces is high enough for statstical analysis, we propose to apply our workflow for the selection of the most relevant roughness parameters to describe the differences between surfaces obtained by different machining parameters and processes.

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“…In addition, multiscale characterizations, which are not limited to any selected bandwidths, multiscale analyses like regression and univariate ANOVA [8], and bootstrap methods could offer insight. These studies need topographical characterizations based on a good understanding of the fundamental interactions in the process, adequate resolutions, and sufficient data for stable statistics.…”

Section: Introductionmentioning

confidence: 99%

Height Fluctuations and Surface Gradients in Topographic Measurements

et al. 2023

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Topographic maps are composed of pixels associated with coordinates (x, y, z) on a surface. Each pixel location (x, y) is linked with fluctuations in a measured height sample (z). Fluctuations here are uncertainties in heights estimated from multiple topographic measurements at the same position. Height samples (z) are measured at individual locations (x, y) in topographic measurements and compared with gradients on topographies. Here, gradients are slopes on a surface calculated at the scale of the sampling interval from inclination angles of vectors that are normal to triangular facets formed by adjacent height samples (z = z(x, y)). Similarities between maps of gradients logs and height fluctuations are apparent. This shows that the fluctuations are exponentially dependent on local surface gradients. The highest fluctuations correspond to tool/material interactions for turned surfaces and to regions of maximum plastic deformation for sandblasted surfaces. Finally, for abraded, heterogeneous, multilayer surfaces, fluctuations are dependent on both abrasion and light/sub-layer interactions. It appears that the natures of irregular surface topographies govern fluctuation regimes, and that regions which are indicative of surface functionality, or integrity, can have the highest fluctuations.

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How to Select 2D and 3D Roughness Parameters at Their Relevant Scales by the Analysis of Covariance

Cited by 4 publications

References 55 publications

Fractal and statistical characterization of brushstroke on paintings

Fractal and statistical characterization of brushstroke on paintings

Systematic selection and evaluation of relevant surface roughness parameters for the characterisation of the innovative RPM-Synchronous Grinding process

Height Fluctuations and Surface Gradients in Topographic Measurements

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