Gear design optimization: Stiffness versus dynamics

Abstract

Optimization is a flexible methodology for gear design since it allows for diverse approaches according to current demands. Thus, lightweight, efficient, small, quiet or robust gears can all be achieved according to the designers’ needs. However, these problems can easily become a computational burden due to the large amount of calculations necessary. In this work, a macro-geometry gear design optimization problem solved by a genetic algorithm is investigated to find the best approach to reach minimum dynamic excitation, comparing as objective functions gear mesh stiffness and dynamic behavior. Given that gear dynamic evaluation can be significantly more computationally expensive than gear mesh stiffness evaluation, the goal is to discuss how optimizing a gear design towards minimum gear mesh stiffness fluctuations compares with optimizing for minimum dynamic excitation. Two gear optimization problems, one more restrictive than the other, are solved with the two objective functions. A genetic algorithm is implemented so that the evolution can be considered equivalent regardless the objective function. From the results obtained, a computationally efficient yet effective gear design optimization approach is proposed.