A Design Method of an Automotive Wheel-Bearing Unit With Discrete Design Variables Using Genetic Algorithms

Abstract

In order to improve the efficiency of the design process and the quality of the resulting design, this study proposes a design method for determining design variables of an automotive wheel-bearing unit of double-row angular-contact ball bearing type by using a genetic algorithm. The desired performance of the wheel-bearing unit is to maximize system life while satisfying geometrical and operational constraints without enlarging mounting space. The design variables selected are number of balls, initial contact angle, standard ball diameter, pitch circle diameter, preload, distance between ball centers, and wheel offset, which must be selected at the preliminary design stage. The use of gradient-based optimization methods for the design of the unit is restricted because this design problem is characterized by the presence of discrete design variables such as the number of balls and standard ball diameter. Therefore, the design problem of rolling element bearings is a constrained discrete optimization problem. A genetic algorithm using real coding and dynamic mutation rate is used to efficiently find the optimum discrete design values. To effectively deal with the design constraints, a ranking method is suggested for constructing a fitness function in the genetic algorithm. A computer program is developed and applied to the design of a real wheel-bearing unit model to evaluate the proposed design method. Optimum design results demonstrate the effectiveness of the design method suggested in this study by showing that the system life of an optimally designed wheel-bearing unit is enhanced in comparison with that of the current design without any constraint violations. It is believed that the proposed methodology can be applied to other rolling element bearing design applications.