A novel multiobjective adaptive surrogate model is employed for the prediction of full-film lubrication performance of surface texture profile for thrust bearings in axial piston pumps. At present, a surface texture optimization design of thrust bearing in axial piston pump has not been provided based on a unique design solutions. In this study, multi-objective adaptive surrogate-based optimization (MO-ASMO) algorithm was used for solving both the mechanical and volumetric losses performance design of surface texture. The non-dominated sorting genetic algorithm (NSGA-II) formed part of the developed multi-objective optimization model, having three competing design objectives which mainly include load capacity, friction torque and leakage rate. A comparative study of two models with accuracy analysis of each case was performed. The results show that the MO-ASMO has the best performance among the different surrogate models. Comparing with the NSGA-II model, the improvement of the leakage rate and the load carrying force made by MO-ASMO are higher about 16.12% and 3.67%, respectively. In other words, multi-objective optimization is capable of enhancing textured slipper bearing performance using MO-ASMO method. The optimal texture radius and texture depth are chosen to minimize the leakage rate and texture bearing capacity.

一种新颖的多目标自适应替代模型用于预测轴向柱塞泵推力轴承的表面纹理轮廓的全膜润滑性能。目前，还没有基于独特的设计方案提供轴向柱塞泵推力轴承的表面纹理优化设计。在这项研究中，基于多目标自适应替代优化（MO-ASMO）算法用于解决表面纹理的机械损耗和体积损耗性能设计。非支配排序遗传算法（NSGA-II）构成了开发的多目标优化模型的一部分，具有三个相互竞争的设计目标，主要包括负载能力，摩擦扭矩和泄漏率。进行了两种模型的比较研究，并对每种情况进行了准确性分析。结果表明，在不同的代理模型中，MO-ASMO的性能最佳。与NSGA-II模型相比，MO-ASMO的泄漏率和承载力的改进分别提高了约16.12％和3.67％。换句话说，多目标优化能够使用MO-ASMO方法增强纹理化的拖鞋轴承性能。选择最佳的纹理半径和纹理深度以最小化泄漏率和纹理承载能力。多目标优化能够使用MO-ASMO方法增强纹理化的拖鞋轴承性能。选择最佳的纹理半径和纹理深度以最小化泄漏率和纹理承载能力。多目标优化能够使用MO-ASMO方法增强纹理化的拖鞋轴承性能。选择最佳的纹理半径和纹理深度以最小化泄漏率和纹理承载能力。