Quantitative Characterisation of Surface Texture

Chiffre, Leonardo De; Lonardo, P.M.; Trumpold, H.; Lucca, D.A.; Goch, G.; Brown, Christopher; Raja, J.; Hansen, Hans Nørgaard

doi:10.1016/s0007-8506(07)63458-1

Cited by 172 publications

(100 citation statements)

References 70 publications

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“…Choosing a set of significant parameters which are functionally correlated is a notoriously difficult topic. The solutions in engineering are usually based on a large amount of empirical experimental data [8]. Very little progress has been made in this area except where a excellent correlation model has been developed, by which the functionalities and the surface geometric characteristics are connected.…”

Section: Introductionmentioning

confidence: 99%

“…Very little progress has been made in this area except where a excellent correlation model has been developed, by which the functionalities and the surface geometric characteristics are connected. In the recent few years, some generic methods have been suggested or used, such as parameter classification [6,8], Analysis of Variances (ANOVA) and correlation analysis [9]. In this case study, the ANOVA method is adopted as a tool to show functional correlation.…”

Section: Introductionmentioning

confidence: 99%

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Numerical characterisation of biomedical titanium surface texture using novel feature parameters

Wang

Jiang

Gurdak

et al. 2011

Wear

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In biomedicine, titanium materials with specific surface textures (for example, those produced using consecutive polishing, sandblasting and acid etching) are widely used to facilitate osseointegration of human/animal tissue with implant surface structures. The surface texture has a critical role on the material"s functionality in terms of cellular adhesion and proliferation. However, conventional surface topography characterisation parameters pertinent to cellular attachment such as Sds (density of summits of a surface) and Ssc (arithmetic mean summit curvature of a surface) are liable to be influenced by low amplitude high spatial frequency components or measurement noise. In this research, a novel feature characterisation, based on pattern recognition techniques is implemented on specially processed titanium surfaces. The mean dimensions and densities of the micro-scale features of the surfaces are extracted. The statistical analysis results demonstrate the efficiency and stability of the feature analysis compared to the use of conventional surface texture parameters. Additionally, potentially efficient parameters for characterisation of biomedical surfaces are indicated through the use of a one-way analysis of variance. As a case study, from the point of view of surface metrology, limited experimental data is presented; the intention of the authors is to give a guide to innovative use of the novel surface characterisation techniques. A large amount of biomedical experiments would be needed in the future to fully validate the correlation between the surface texture parameters and its biomedical functions but the present work provides a useful start point for a larger study.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical characterisation of biomedical titanium surface texture using novel feature parameters

Wang

Jiang

Gurdak

et al. 2011

Wear

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“…7) generated on the machined surface [2,3]. The maximum depth of valleys is between 0.35 and 0.55 μm and their widths 0.08 and 0.16 μm depending on the sequence of machining operations performed.…”

Section: Characterization Of Function-related Parametersmentioning

confidence: 99%

“…Special attention in this area has been given during two past decades to the standardization of 3D set roughness parameters [1][2][3]. Precision machining with Rz=2.5-4 μm and high-precision machining with Rz<1 μm of hardened steels (45-60 HRC) with ultrahard cutting tool materials have been developed with a special interest to automotive, hydraulic, and die and mold industry sectors [4,5] due to high flexibility, possible complete machining, lesser ecological impact, and higher MRR [6,7].…”

Section: Introductionmentioning

confidence: 99%

Characterization of surface textures generated on hardened steel parts in high-precision machining operations

Grzesik

Rech

Żak

2015

Int J Adv Manuf Technol

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This paper presents the comparison of surface textures produced by high-precision cutting and abrasive processes on hardened steel parts. Investigations include surfaces generated by hard turning, belt grinding, and superfinishing (external honing) operations. As a result, several surfaces generated with different process kinematics and the Sa roughness parameter of about 0.05 μm were compared. Apart from the standard 2D and 3D roughness parameters, the fractal, motif, and frequency parameters were considered.

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“…[6] Surface roughness is often treated as an index of the product quality [7]. The surface roughness parameters may be used to explain the functional properties of the surfaces under mating [8][9]. The 2D surface parameters though used widely are not enough to explain the functionality of surfaces as the surfaces interact in three dimensions rather than in two dimensions [10].…”

Section: Introductionmentioning

confidence: 99%

Multicriteria Optimization for Determining Low Wear of the Surface Produced in Electrical Discharge Machining

Sivaprasad¹

2017

IJRET

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Quantitative Characterisation of Surface Texture

Cited by 172 publications

References 70 publications

Numerical characterisation of biomedical titanium surface texture using novel feature parameters

Numerical characterisation of biomedical titanium surface texture using novel feature parameters

Characterization of surface textures generated on hardened steel parts in high-precision machining operations

Multicriteria Optimization for Determining Low Wear of the Surface Produced in Electrical Discharge Machining

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