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Gear design techniques usually results on heavy drives that are contradictory to the tendency of mass reduction on the modern road vehicles applications. This paper proposes the utilization of a genetic algorithm to design and optimize geometric parameters of a transmission stage comprised of two steel gears. Although genetic algorithm is not an optimization technique on a straight sense, its use is increasing in several applications where the design space is multidimensional. In this study, the first fitness function proposed is a weighted average of critical performance factors on gears design such as total drive weight, tooth contact stress, bending stress and total space occupied by the assembly and the second fitness proposed considers the center distance influence and the tooth contact stress and bending stress. The weights of the average are modified in several simulations.

Since its advent in the years 1960's, finite element analysis (FEA) has been the main tool for industries to assess components and systems failure and physical behavior. Increased computational resources, the maturity of software packages and the need for more complex applications have contributed to add new resources to the finite element scenario. One of the most interesting of those new features is structural optimization, which takes product development to a whole new level by reducing material costs while considering geometrical and manufacturing constraints. In the present paper, structural optimization is used as a tool to develop a new lever arm for clutch release bearings. Among the design constraints, geometric restraints as well as loading and contact interfaces were considered.