Surface texture design is crucial for enhancing the tribological performance in applications involving sliding surfaces. This research presents a generative design approach for creating optimal surface textures that maximize hydrodynamic lubrication efficiency. By integrating a Moving Morphable Void (MMV)-based explicit topology optimization framework, we harness the power of computational algorithms to explore a vast design space and generate innovative texture patterns that enhance the load-carrying capacity (LCC) of hydrodynamic bearings. The methodology introduces multi-sets of voids to represent diverse film thickness profiles within the texture, allowing for a high degree of design flexibility. The explicit geometric information provided by the MMV approach ensures compatibility with CAD systems, facilitating the precise and efficient translation of optimized designs into manufacturable surfaces. To demonstrate the effectiveness of our generative design strategy, we provide a suite of numerical simulations that illustrate the performance benefits of the proposed textures. These are complemented by experimental studies that validate the theoretical findings and confirm the practical viability of the optimized surface textures.
