Static Friction and Junction Growth of Contacting Three-dimensional Sinusoidal Asperities and Rough surfaces

Abstract

It is reported that about one third of the world's primary energy per year was consumed by friction, and about 55% of the machine parts failed due to wear and tear. Therefore, it is important to understand the process involved in friction and to obtain accurate models that predict the friction. Many researchers have developed different kinds of models to solve this problem. If researchers are successful, engineers could use the results predicted by the models, to increase or decrease the desirable friction in a controlled manner. In this study, the sinusoidal shaped asperity is considered instead of considering the spherical shaped asperity considered in most of the existing statistical models. A study of the sinusoidal surface contact is investigated comprehensively. First, the contact behaviors of a single three-dimensional sinusoidal asperity under normal loading for elastic contact are investigated analytically. The equations for complete contact pressure and critical amplitude of sinusoidal surface under the full stick condition are derived. Then, the effects of contact conditions, material properties and geometric parameter are studied by using a finite element method (FEM). Next, the contact between a deformable sinusoidal surface and a rigid flat under combined normal and tangential loading is investigated. The effects of material properties, geometric parameter, contact pressure and critical shear strength on static friction coefficient and junction growth are studied. The empirical equations for the static friction coefficient and junction growth are provided. The contact and friction of rough surface contact are then studied by using FEM. First, the spectral interpolation method is presented, and the effect of sampling resolution on the behavior of contact and friction are investigated. A multi-scale model that predicts static friction coefficient is developed. The results are then compared with FEM data and statistical models.