Multi-objective design optimisation of rolling bearings using genetic algorithms

Abstract The design of rolling bearings has to satisfy various constraints, e.g. the geometrical, kinematics and the strength, while delivering excellent performance, long life and high reliability. This invokes the need of an optimal design methodology to achieve these objectives collectively, i.e. the multi-objective optimisation. In this paper, three primary objectives for a rolling bearing, namely, the dynamic capacity (Cd), the static capacity (Cs) and the elastohydrodynamic minimum film thickness (Hmin) have been optimized separately, pair-wise and simultaneously using an advanced multi-objective optimisation algorithm: NSGA II (non-dominated sorting based genetic algorithm). These multiple objectives are performance measures of a rolling bearing, compete among themselves giving us a trade-off region where they become “simultaneously optimal”, i.e. Pareto optimal. A sensitivity analysis of various design parameters has been performed, to see changes in bearing performance parameters, and results show that, except the inner groove curvature radius, no other design parameters have adverse affect on performance parameters.