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Automotive wheel bearings have primary functions of translating the rotating motion of the wheels into linear vehicle motion while bearing the vehicle weight. Bearing life is affected by many parameters such as bearing geometry, vehicle and bearing technical specifications, driving conditions, lubrication conditions, material properties, and so on. In this paper, the optimization of bearing life was performed. Optimization formulation with no constraints was established to solve this problem. The large scale generalized reduced gradient, LSGRG, algorithm was applied to calculate the basic rating life and modified rating life proposed by ISO 281:1990 and ISO 281:2007, respectively. The optimization history for design variables and cost function were investigated. The results showed that the optimized basic rating life and modified rating life increased about 15% and 31% comparing with those of initial life, respectively.

Automotive wheel bearings have the primary function of translating the rotating motion of the wheels into linear vehicle motion while supporting the vehicle weight. As vehicle lives continue to increase, there is a need for longer service lives than those of existing products. There is an even greater need for performance-related reliability during usage. Lateral stiffness, one of the main parameters of wheel bearing design, has a significant influence on ride comfort and steering feel. In this study, reliability-based weight optimization considering geometric uncertainty for automotive wheel bearings was investigated. Deterministic design optimization (DDO) and reliability-based design optimization (RBDO) were performed. For optimization, the following three key relationships were chosen: wheel bearing specification and geometry for design variables, weight for cost function, and stiffness for constraint.