Comparison of fractal and profilometric methods for surface topography characterization

Poljaček, Sanja Mahović; Risović, Dubravko; Furić, Krešimir; Gojo, Miroslav

doi:10.1016/j.apsusc.2007.11.040

Cited by 90 publications

(17 citation statements)

References 20 publications

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“…However, the differences for the depth‐related parameters are significantly higher (in the range 10%–60% with respect to TR200) and do not diminish with the number of imprints as should be expected based on physical models of surface flattening. The observed differences between the values of depth‐related parameters obtained by stylus profilometry and the merged‐image method, although seemingly high, are also in the range encountered in the comparison of results obtained by classical contact (stylus) and non‐contact (laser line and surface) profilometry, where they may be even higher . Moreover, the relative ratios between the parameters and respective trends are in better agreement for the direct and indirect profilometric methods considered here than between the contact and non‐contact methods mentioned previously (ibid.).…”

Section: Resultssupporting

confidence: 59%

“…The proportionality constant is derived from the appropriate calibration that was previously mentioned. A similar approach was used previously in the image‐based fractal description of microstructures …”

Section: Resultsmentioning

confidence: 99%

“…The advantage of this concept lies in its insensitivity to the structural details as well as in the fact that the structure is characterized by a single descriptor only, the fractal dimension . The fractal dimension can be conveniently estimated from SEM or AFM images/micrographs and seems to be well correlated to the profilometric parameters . Moreover, it is related to basic material properties and different mechanisms that influence the surface topography .…”

Section: Introductionmentioning

confidence: 99%

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Comparative study of direct and indirect image‐based profilometry in characterization of surface roughness

Pavlović

Risović

Novaković

2012

Surface & Interface Analysis

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a In this study, we have investigated and compared two different approaches to the characterization of surface structure and roughness. The results of the stylus profilometric method are compared with the results obtained using a relatively novel, indirect, image-based profilometry. The aim was to evaluate the performance and practical usefulness of the indirect method in the characterization of a surface topography. The indirect approach involved the use of Gwyddion software for analysis of scanning electron microscope (SEM) images and calculation of standard profilometric parameters. It is well known that SEM micrographs provide an excellent tool for visualization and qualitative description of surface topography, including the estimation of corresponding fractal dimensions. The results of this study demonstrate that it is also possible to obtain profilometric parameters from analysis of SEM micrographs with appropriately calibrated gray scale intensity distributions, and that the values of the parameters are comparable to those obtained by classical (stylus) profilometry. Better agreement with results of the direct profilometric method was achieved for parameters related to the distribution of heights than those related to the distribution of depths. Regarding these differences, we have provided arguments indicating that the values of these depths -related parameters obtained by indirect profilometry -are closer to the "true" values than those inferred from the direct profilometry. Generally, the results of this comparative study indicate that indirect image-based profilometry is a valuable and efficient tool in the characterization of various surface's topographies.

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Section: Resultssupporting

confidence: 59%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative study of direct and indirect image‐based profilometry in characterization of surface roughness

Pavlović

Risović

Novaković

2012

Surface & Interface Analysis

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“…Hence, three-dimensional surface topography parameters are necessary for assessing the surface roughness characteristics more effectively [3]. According to Mahovic Poljacek et al [4], a precise characterization of roughness and surface topography is of prime importance in many engineering industries because certain functional properties of the materials are often determined by the surface structure and characteristics. Estimation of the magnitude of surface roughness under the given cutting conditions resulting from metal removal operations is one of the major goals in this area [5,6].…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis by Measurement of Surface Roughness Variations in Turning Process of Duplex Stainless Steel

Królczyk¹,

Legutko²

2014

Metrology and Measurement Systems

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The objective of the investigation was to identify surface roughness after turning with wedges of coated sintered carbide. The investigation included predicting the average surface roughness in the dry machining of Duplex Stainless Steel (DSS) and the determination of load curves together with roughness profiles for various cutting conditions. The load curves and roughness profiles for various cutting wedges and variable cutting parameters were compared. It has been shown that dry cutting leads to a decrease in friction for lubricated surfaces, providing a small initial contact area where the surface is contacted. The study has been performed within a production facility during the production of electric motor parts and deep-well pumps.

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“…The selection of particular magnification has no influence on estimation of fractal dimension as we have shown previously (Mahović‐Poljaček et al . ). The dimension of micrographs was 2,135 × 1,592 pixels (width × height).…”

Section: Materials Theory and Methodsmentioning

confidence: 99%

Performance assessment of methods for estimation of fractal dimension from scanning electron microscope images

Risović

Pavlović²

2013

Scanning

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Processing of gray scale images in order to determine the corresponding fractal dimension is very important due to widespread use of imaging technologies and application of fractal analysis in many areas of science, technology, and medicine. To this end, many methods for estimation of fractal dimension from gray scale images have been developed and routinely used. Unfortunately different methods (dimension estimators) often yield significantly different results in a manner that makes interpretation difficult. Here, we report results of comparative assessment of performance of several most frequently used algorithms/methods for estimation of fractal dimension. To that purpose, we have used scanning electron microscope images of aluminum oxide surfaces with different fractal dimensions. The performance of algorithms/methods was evaluated using the statistical Z-score approach. The differences between performances of six various methods are discussed and further compared with results obtained by electrochemical impedance spectroscopy on the same samples. The analysis of results shows that the performance of investigated algorithms varies considerably and that systematically erroneous fractal dimensions could be estimated using certain methods. The differential cube counting, triangulation, and box counting algorithms showed satisfactory performance in the whole investigated range of fractal dimensions. Difference statistic is proved to be less reliable generating 4% of unsatisfactory results. The performances of the Power spectrum, Partitioning and EIS were unsatisfactory in 29%, 38%, and 75% of estimations, respectively. The results of this study should be useful and provide guidelines to researchers using/attempting fractal analysis of images obtained by scanning microscopy or atomic force microscopy.

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Comparison of fractal and profilometric methods for surface topography characterization

Cited by 90 publications

References 20 publications

Comparative study of direct and indirect image‐based profilometry in characterization of surface roughness

Comparative study of direct and indirect image‐based profilometry in characterization of surface roughness

Experimental Analysis by Measurement of Surface Roughness Variations in Turning Process of Duplex Stainless Steel

Performance assessment of methods for estimation of fractal dimension from scanning electron microscope images

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