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Purpose The purpose of this paper is to study the effect of liner surface texture on journal bearing performance. Modeling the profile curvature of the dimples or grooves is planned for different cases of texture surface under thermo-hydrodynamic condition (THD). The aim of this paper is to determine the effect the texture surface on the performance of journal bearing and specify the optimum shape for texture dimples. Design/methodology/approach The paper was opted for an exploratory study by applying finite difference method to solve the energy equation, the heat conduction equations and the Reynolds equation numerically. The lubricant film thickness is divided to a mesh of 640,000 points. The equations were solved for each point of the mesh by using a MATLAB code. For texture shape optimization, 24 cases of different texture shapes were selected which includes elliptical, triangle and square curvature shape. Findings The paper provides theoretical insights about the effect of texture shape on journal bearing performance. It was concluded that to get a high load-carrying capacity, the direction of curvature is preferably to be perpendicular to the sliding direction. The convex texture has higher load carrying capacity than concave texture. Finally, the surface with textures in channel form yields better overall performance than the surface with several dimples. Originality/value This paper fulfils an identified need to study how texture surface affects the performance of journal bearing under thermo-hydrodynamic conditions.

Nylon 66 has been widely used for numerous mechanical applications but its sliding wear mechanisms are not fully understood. In particular, limited attention has been paid to the generation of fatigue surface cracks under constant and cyclic load conditions. The present work focuses on the effect of load frequency on the wear behavior of a polymer with surface defects in dry sliding conditions. The defects were imposed vertical deep cracks perpendicular to the direction of sliding. Wear studies were conducted against a steel counterface at constant loads, and in cyclic loads at different frequencies. Artificial neural network (ANN) models were examined to identify one that optimally simulates wear under the applied load parameters.Surface cracks were found to have a remarkable adverse effect on the wear behavior of the polymer. The wear rates were influenced by the number of cracks as well as the type of applied load. Furthermore, results suggest that the presence of surface cracks is attributable to the section B wear regime. Finally, acceptable predicted wear rate values were obtained by introducing the ANN wear model.

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