Surface texture has been employed to improve the performance of the sliding bearings. The texture is introduced on the surface in the form of innumerable micro-geometries i.e., dimples, grooves or pockets. This paper attempts to find the optimal parameters, namely, depth, dimensions, extent in radial and circumferential directions of rectangular dimples to enhance the performance behavior of sector shape pad thrust bearing. Reynolds equation incorporating mass-conservation algorithm is used as a mathematical model and then Artificial Neural Network model is applied to predict the performance parameters, namely load carrying capacity and coefficient of friction. Lastly, Genetic Algorithms is employed to optimize the dimple parameters with the fitness evaluations based on the prediction model. The findings illustrate a remarkable enhancement in load-carrying capacity alongside a substantial reduction in the coefficient of friction.
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