Abstract

Surface texturing has proved to be a very useful tool for expanding the behaviour under hydrodynamic and elastohydrodynamic regimes instead of mixed or boundary lubrication regimes, and therefore for reducing the friction coefficient under high-load low-speed conditions. This article presents the texturing of different copper test-samples using photolithography and chemical etching to measure the friction coefficient using a point contact machine. The effects of texture size, texturing density, the initial roughness of the samples and the operating conditions have all been studied. Some combinations of texturing density and texture size achieve up to 30% reduction in the friction coefficient. Taking into account experimental data, artificial neural networks are used as a tool for both predicting and optimising the friction coefficient on the textured surface for any given operating condition.