scholarly journals On the influence of surface roughness on real area of contact in normal, dry, friction free, rough contact by using a neural network

Wear ◽  
2009 ◽  
Vol 266 (5-6) ◽  
pp. 592-595 ◽  
Author(s):  
M.P. Rapetto ◽  
A. Almqvist ◽  
R. Larsson ◽  
P.M. Lugt
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Dry Friction ◽  
Real Area Of Contact ◽  
Real Area

The Influence of Surface Roughness on the Mechanism of Friction

1970 ◽  
Vol 92 (2) ◽  
pp. 264-272 ◽  
Author(s):  
T. Tsukizoe ◽  
T. Hisakado
Keyword(s):  
Surface Roughness ◽  
Metal Surface ◽  
Coefficient Of Friction ◽  
Dry Friction ◽  
Metal Surfaces ◽  
Roughness Effects ◽  
Real Area Of Contact ◽  
The Coefficient Of Friction ◽  
Tangential Forces ◽  
Soft Metal

A study was made of surface roughness effects on dry friction between two metals, assuming that the asperities are cones of the slopes which depend on the surface roughness. The theoretical explanations were offered for coefficients of friction of the hard cones and spheres ploughing along the soft metal surface. A comparison of calculated values based on these with experimental data shows good agreement. Moreover, theoretical discussion was carried out of surface roughness effects on dry friction between two metal surfaces on the basis of the analyses of the frictional mechanism for a hard slider on the metal surface. The theoretical estimation of the coefficient of friction between two metal surfaces can be carried out by using the relations between the surface roughness and the slopes of the asperities, and the coefficient of friction due to the adhesion at the interface. The experiments also showed that when two metal surfaces are first loaded normally and then subjected to gradually increasing tangential forces, real area of contact between them increases and the maximum tangential microslip of them increases with the increase of the surface roughness.

Static aging vs. dynamic rejuvenation in solid friction

2002 ◽  
Vol 12 (9) ◽  
pp. 269-273
Author(s):  
C. Caroli ◽  
T. Baumberger ◽  
L. Bureau
Keyword(s):  
Dry Friction ◽  
Dynamic Regime ◽  
Adhesive Layers ◽  
Rich Variety ◽  
Real Area Of Contact ◽  
Solid Friction ◽  
Static Regime ◽  
The Rich ◽  
Adhesive Contacts ◽  
Real Area

Dry friction between macroscopic solids can be understood in terms of the elastic (static regime) versus plastic (dynamic regime) responses to a shear stress of a set of dilute micrometric adhesive contacts. When pinned, such a system is the seat of two distinguishablc slow, quasi-logarithmic aging dynamics: - a “geometric" aging process, originating from the bulk of the contacting asperities, which results in the increase of the real area of contact. - a structural one, taking place within the nm-thick adhesive layers, which behave as 2D confined “soft " structural glasses. Both mechanisms give rise to associated dynamic rejuvenation phenomena, akin to those observed in threshold fluids, which govern the rich variety of frictional dynamics exhibited by extended systems.

The Area of Contact Between Rough Surfaces and Flats

1967 ◽  
Vol 89 (1) ◽  
pp. 81-87 ◽  
Author(s):  
J. A. Greenwood
Keyword(s):  
Surface Roughness ◽  
Work Hardening ◽  
Deformation Mode ◽  
Constant Independent ◽  
The Real ◽  
Real Area Of Contact ◽  
Ideal Plastic ◽  
Average Size ◽  
Contact Size ◽  
Real Area

If the real area of contact between surfaces is determined by ideal plastic flow of the microcontacts, then the proportionality between the area of contact and the load follows immediately. If the deformation mode is elastic, or elastic-plastic, or plastic with work-hardening, which will be the usual cases, then the proportionality is harder to explain. However, by considering the statistical distribution of heights of the surface asperities, it can be shown that the average size of a microcontact is almost constant, independent of load; consequently, the fact that the contact pressure at a single micro-contact may vary with contact size becomes irrelevant. If the real origin of the laws of friction is in the statistics of surface roughness and not in a particular mode of deformation, the applicability of the Bowden and Tabor theory of friction to plastics and other nonmetals becomes more readily understandable.

Deformation of Rough Line Contacts

1999 ◽  
Vol 121 (3) ◽  
pp. 449-454 ◽  
Author(s):  
E. R. M. Gelinck ◽  
D. J. Schipper
Keyword(s):  
Surface Roughness ◽  
Pressure Distribution ◽  
Half Width ◽  
Central Pressure ◽  
Bulk Deformation ◽  
The Real ◽  
Good Parameter ◽  
Real Area Of Contact ◽  
Line Contacts ◽  
Real Area

The influence of surface roughness on the bulk deformation of line contacts is studied. The model of Greenwood and Tripp (1967) will be extended to line contacts. It is found that the central pressure is a very good parameter to characterize the pressure distribution of rough line contacts. Function fits of the central pressure, the effective half width, the real area of contact, and the number of contacts are made. Comparison is made with the work of Lo (1969) and Greenwood et al. (1984).

Microcontact Model for Paper-Based Wet Friction Materials

2001 ◽  
Vol 124 (2) ◽  
pp. 414-419 ◽  
Author(s):  
H. Gao ◽  
G. C. Barber
Keyword(s):  
Surface Roughness ◽  
Density Distribution ◽  
Friction Material ◽  
Friction Materials ◽  
The Real ◽  
Real Area Of Contact ◽  
Wet Friction ◽  
Gaussian Density ◽  
Negative Skewness ◽  
Real Area

This paper is focused on the real area of contact for paper-based wet friction materials during the engagement of wet clutches. The deformation of the wet friction material is identified as elastic during the engagement. A microcontact model is proposed considering both surface roughness and skewness. A Weibull density distribution is employed in the model rather than a Gaussian density distribution. This model is compared with the Greenwood-Williamson (GW) model for the cases of positive skewness, zero skewness and negative skewness. The real areas of contact of new, run-in and glazed wet friction materials were investigated using this microcontact model. Both surface roughness and skewness were found to have a great effect on the real area of contact.

A Study of the Relative Surface Conformity Between Two Surfaces in Sliding Contact

1991 ◽  
Vol 113 (4) ◽  
pp. 755-761 ◽  
Author(s):  
Fu-Xing Wang ◽  
P. Lacey ◽  
R. S. Gates ◽  
S. M. Hsu
Keyword(s):  
Surface Roughness ◽  
Computer Program ◽  
Contact Area ◽  
Sliding Contact ◽  
Test Duration ◽  
Lubrication Mechanism ◽  
The Real ◽  
Wear Process ◽  
Real Area Of Contact ◽  
Real Area

The surface roughnesses of two surfaces in a wear contact can change throughout the course of the wear process. This may or may not change the lubrication mechanism of the system depending on the real area of contact as influenced by the changes in the surface roughness. The present work examines the changes in surface roughness within the contact area, as well as the relative mating of the two surfaces. To quantify the similarity between the two wear surfaces, a new concept, the relative surface conformity, has been defined and developed. To effectively measure this parameter, a computer program was written to input the wear scar profilometry traces and to calculate the relative surface conformity of the two. Finally, the relative surface conformity was shown to rise with increasing test duration, during running in.

Prediction of Surface Roughness in Magneto Rheological Abrasive Flow Finishing Process by Artificial Neural Networks and Regression Analysis

2015 ◽  
Vol 766-767 ◽  
pp. 1076-1084
Author(s):  
S. Kathiresan ◽  
K. Hariharan ◽  
B. Mohan
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Regression Analysis ◽  
Back Propagation ◽  
Hydraulic Pressure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Magneto Rheological ◽  
Hidden Neurons ◽  
Abrasive Flow Finishing

In this study, to predict the surface roughness of stainless steel-304 in Magneto rheological Abrasive flow finishing (MRAFF) process, an artificial neural network (ANN) and regression models have been developed. In this models, the parameters such as hydraulic pressure, current to the electromagnet and number of cycles were taken as variables of the model.Taguchi’s technique has been used for designing the experiments in order to observe the different values of surface roughness . A neural network with feed forward with the help of back propagation was made up of 27 input neurons, 7 hidden neurons and one output neuron. The 6 sets of experiments were randomly selected from orthogonal array for training and residuals were used to analyze the performance. To check the validity of regression model and to determine the significant parameter affecting the surface roughness, Analysis of variance (ANOVA) andF-test were made. The numerical analysis depict that the current to the electromagnet was an paramount parameter on surface roughness.Key words: MRAFF, ANN, Regression analysis

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