Abstract
Many porous bearing or tribological component designs require extensive numerical analyses which usually is followed by trial testing of prototypes before the final production. If chosen properly tribological models and optimization methods can be a practical and effective tool for the design tasks. This study presents a multiobjective optimization algorithm, two-stage group inching fortification (GIF) method, to solve a porous air bearing design. A comparison of the proposed approach with a genetic algorithm (GA) and hyper-cube dividing method (HDM) is conducted. The results show that the Pareto solution set obtained by the first- and two-stage GIF methods have a wider spread of Pareto front with a reduced number of objective-function calls than by using the GA or HDM.
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