An Optimal Design Methodology of Tapered Roller Bearings Using Genetic Algorithms

Abstract

In the design of tapered roller bearings, long life is the one of the most important criterion. The design of bearings has to satisfy constraints of geometry and strength, while operating at its rated speed. An optimal design methodology is needed to achieve this objective (i.e., the maximization of the fatigue life). The fatigue life is directly proportional to the dynamic capacity; hence, for the present case, the latter has been chosen as the objective function. It has been optimized by using a constrained nonlinear formulation with real-coded genetic algorithms. Design variables for the bearing include four geometrical parameters: the bearing pitch diameter, the diameter of the roller, the effective length of the roller, and the number of rollers. These directly affect the dynamic capacity of tapered roller bearings. In addition to these, another five design constraint constants are included, which indirectly affect the basic dynamic capacity of tapered roller bearings. The five design constraint constants have been given bounds based on the parametric studies through initial optimization runs. There is good agreement between the optimized and standard bearings in respect to the basic dynamic capacity. A convergence study has been carried out to ensure the global optimum point in the design. A sensitivity analysis of various design parameters, using the Monte Carlo simulation method, has been performed to see changes in the dynamic capacity. Illustrations show that none of the geometric design parameters have adverse affect on the dynamic capacity.

Keywords:

• Tapered roller bearings
• Optimum design
• Genetic algorithms
• Parametric sensitivity analysis
• Monte Carlo simulations