The Effect of Determining Topography Parameters on Analyzing Elastic Contact Between Isotropic Rough Surfaces

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Gorakh Pawar ◽  
Pawel Pawlus ◽  
Izhak Etsion ◽  
Bart Raeymaekers
Keyword(s):  
Rough Surface ◽  
Cross Sections ◽  
Nearest Neighbor ◽  
Rough Surfaces ◽  
Sampling Interval ◽  
Elastic Contact ◽  
Spectral Moments ◽  
Identification Scheme ◽  
Contact Parameters ◽  
Identification Model

The elastic contact between two computer generated isotropic rough surfaces is studied. First the surface topography parameters including the asperity density, mean summit radius, and standard deviation of asperity heights of the equivalent rough surface are determined using an 8-nearest neighbor summit identification scheme. Second, many cross sections of the equivalent rough surface are traced and their individual topography parameters are determined from their corresponding spectral moments. The topography parameters are also obtained from the average spectral moments of all cross sections. The asperity density is found to be the main difference between the summit identification scheme and the spectral moments method. The contact parameters such as the number of contacting asperities, real area of contact, and contact load for any given separation between the equivalent rough surface and a rigid flat are calculated by summing the contributions of all the contacting asperities using the summit identification model. These contact parameters are also obtained with the Greenwood-Williamson (GW) model using the topography parameters from each individual cross section and from the average spectral moments of all cross sections. Three different surfaces and three different sampling intervals were used to study how the method to determine topography parameters affects the resulting contact parameters. The contact parameters are found to vary significantly based on the method used to determine the topography parameters, and as a function of the autocorrelation length of the surface, as well as the sampling interval. Using a summit identification model or the GW model based on topography parameters obtained from a summit identification scheme is perhaps the most reliable approach.

Strongly Anisotropic Rough Surfaces

1979 ◽  
Vol 101 (1) ◽  
pp. 15-20 ◽  
Author(s):  
A. W. Bush ◽  
R. D. Gibson ◽  
G. P. Keogh
Keyword(s):  
Random Process ◽  
Rough Surface ◽  
Contact Area ◽  
Process Model ◽  
Rough Surfaces ◽  
Plasticity Index ◽  
Real Contact Area ◽  
Elastic Contact ◽  
Elastic Plastic ◽  
Real Contact

The statistics of a strongly anisotropic rough surface are briefly described. The elastic contact of rough surfaces is treated by approximating the summits of a random process model by parabolic ellipsoids and applying the Hertzian solution for their deformation. Load and real contact area are derived as functions of the separation and for all separations the load is found to be approximately proportional to the contact area. The limits of elastic/plastic contact are discussed in terms of the plasticity index.

Topography Analysis of Random Anisotropic Gaussian Rough Surfaces

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Deepak K. Prajapati ◽  
Mayank Tiwari
Keyword(s):  
Standard Deviation ◽  
Rough Surface ◽  
Correlation Length ◽  
Rough Surfaces ◽  
Spectral Moments ◽  
Statistical Characterization ◽  
Wear Process ◽  
Increase With Increase ◽  
Bandwidth Parameter ◽  
Significant Difference

Engineered surfaces (ground and similarly structured rough surfaces) show anisotropic characteristics and their topography parameters are direction dependent. Statistical characterization of these surfaces is still complex because of directional nature of surfaces. In this technical brief, an attempt is made to simulate anisotropic surfaces through use of topography parameters (three-dimensional (3D) surface parameters). First, 3D anisotropic random Gaussian rough surface is generated numerically with fast Fourier transform (FFT). Numerically generated anisotropic random Gaussian rough surface shows statistical properties (texture direction, texture ratio) similar to ground and similarly directional anisotropic rough surfaces. For numerically generated anisotropic Gaussian rough surface, important 3D roughness parameters are determined. Sayles and Thomas' (1976, “Thermal Conductance of Rough Elastic Contact,” Appl. Energy, 2(4), pp. 249–267.) theoretical model for directional anisotropic rough surface is adopted here for calculating the summit parameters, i.e., equivalent bandwidth parameter, mean summit curvature, skewness of summit height, standard deviation of summit height, and equivalent spectral moments. This work demonstrates the variation of spectral moments in both across and parallel to the lay directions with pattern ratio (γ=βx/βy). Correlation length (βx) is fixed 10μm and correlation length (βy) is varied from 100 to 10 μm. Variation of summit parameters with pattern ratio is also discussed in detail. Results shows that mean summit curvature and skewness of summit heights increase with increase in pattern ratio, whereas standard deviation of summit heights and equivalent bandwidth parameter (αe) decreases with pattern ratio. A significant difference is found in “Abbott-Firestone” parameters when calculated in both perpendicular and parallel to lay directions. Effect of these parameters on wear process is discussed in brief.

Analysis of Normal Elastic Contact Stiffness of Rough Surfaces Based on Ubiquitiform Theory

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Shaofei Shang ◽  
Xiaoshan Cao ◽  
Zhiqiang Liu ◽  
Junping Shi
Keyword(s):  
Lower Bound ◽  
Rough Surface ◽  
Scale Invariance ◽  
Rough Surfaces ◽  
Contact Stiffness ◽  
Elastic Contact ◽  
Normal Contact ◽  
Normal Stiffness ◽  
Normal Contact Stiffness ◽  
The Impact

Abstract In this study, the normal stiffness of elastic contact between rough surfaces with asperities following Gaussian distribution is investigated using ubiquitiform theory, developed from fractal theory. In the generalized ubiquitiformal Sierpinski carpet model, the rough surface including contact asperity is controlled for, given the lower bound to scale invariance of rough surfaces. Considering the stiffness of a single asperity deduced from the Hertz contact model, we deduce the theoretical relation between the normal stiffness and the elastic contact of rough surfaces based on ubiquitiform theory. The results show that the normal contact stiffness of a rough surface increases as the normal load rises. If the ubiquitiformal complexity of a rough surface increases or the lower bound to scale invariance of a rough surface decreases, the normal contact stiffness of the rough surface should increase. The larger the ubiquitiformal complexity of a rough surface is, the more obvious the impact of the lower bound to scale invariance on the normal contact stiffness of the rough surface becomes. The results based on the ubiquitiformal model and the experimental results are in closer agreement. Therefore, the introduction of scale invariance is crucial to the surface contact problem.

Numerical Analysis for the Elastic Contact of Real Rough Surfaces

1999 ◽  
Vol 42 (3) ◽  
pp. 443-452 ◽  
Author(s):  
Yuan-Zhong Hu ◽  
Gary C. Barber ◽  
Dong Zhu
Keyword(s):  
Numerical Analysis ◽  
Rough Surfaces ◽  
Elastic Contact

scholarly journals A Static Friction Model for Unlubricated Contact of Random Rough Surfaces at Micro/Nano Scale

Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 368
Author(s):  
Shengguang Zhu ◽  
Liyong Ni
Keyword(s):  
Rough Surfaces ◽  
Static Friction ◽  
Friction Model ◽  
Elastic Contact ◽  
Nano Scale ◽  
Random Rough Surfaces ◽  
Proposed Model ◽  
Dissipation Mechanism ◽  
Rigid Plane ◽  
Contact Situation

A novel static friction model for the unlubricated contact of random rough surfaces at micro/nano scale is presented. This model is based on the energy dissipation mechanism that states that changes in the potential of the surfaces in contact lead to friction. Furthermore, it employs the statistical theory of two nominally flat rough surfaces in contact, which assumes that the contact between the equivalent rough peaks and the rigid flat plane satisfies the condition of interfacial friction. Additionally, it proposes a statistical coefficient of positional correlation that represents the contact situation between the equivalent rough surface and the rigid plane. Finally, this model is compared with the static friction model established by Kogut and Etsion (KE model). The results of the proposed model agree well with those of the KE model in the fully elastic contact zone. For the calculation of dry static friction of rough surfaces in contact, previous models have mainly been based on classical contact mechanics; however, this model introduces the potential barrier theory and statistics to address this and provides a new way to calculate unlubricated friction for rough surfaces in contact.

Reduced Rough-Surface Parametrization for Use With the Discrete-Element Model

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
Stephen T. McClain ◽  
Jason M. Brown
Keyword(s):  
Heat Transfer ◽  
Rough Surface ◽  
Surface Characterization ◽  
Rough Surfaces ◽  
Roughness Element ◽  
Three Dimensional ◽  
Element Model ◽  
Discrete Element ◽  
Diameter Distribution ◽  
Discrete Element Model

The discrete-element model for flows over rough surfaces was recently modified to predict drag and heat transfer for flow over randomly rough surfaces. However, the current form of the discrete-element model requires a blockage fraction and a roughness-element diameter distribution as a function of height to predict the drag and heat transfer of flow over a randomly rough surface. The requirement for a roughness-element diameter distribution at each height from the reference elevation has hindered the usefulness of the discrete-element model and inhibited its incorporation into a computational fluid dynamics (CFD) solver. To incorporate the discrete-element model into a CFD solver and to enable the discrete-element model to become a more useful engineering tool, the randomly rough surface characterization must be simplified. Methods for determining characteristic diameters for drag and heat transfer using complete three-dimensional surface measurements are presented. Drag and heat transfer predictions made using the model simplifications are compared to predictions made using the complete surface characterization and to experimental measurements for two randomly rough surfaces. Methods to use statistical surface information, as opposed to the complete three-dimensional surface measurements, to evaluate the characteristic dimensions of the roughness are also explored.

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