Optimum design of a spur gear using a two level optimization approach

Abstract

In this paper, we present a two level optimization approach in order to enhance the design process of a one-stage speed reducer. The proposed design methodology is performed using genetic algorithms which are judiciously combined with the use of :i) analytical models (1stlevel) and ii) Finite Element Method (FEM)based models ( 2nd level), to evaluate design candidates. Indeed, the use of CAD-CAE tools to develop higher fidelity FEM models allows to re-evaluate the attained first level designs, while accounting for new design parameters and advanced aspects which have been ignored in the first level. In order to minimize the computational burden, a metamodel based optimization technique is adopted at this second level. To illustrate the efficiency of the proposed approach, a case study of a spur gear based reducer is presented where the design of experiments is built using Hypercube Latin Sampling and surrogate models are constructed using Radial Basic Functions.

Archive of Mechanical Engineering

“…The conclusion shows that the optimal gear weight in [9] is smaller. Research in [10] optimizes the design of the one-stage speed reducer with the design variables and the target function like that in [8]. Still, it is implemented in a two-level optimization.…”