Title: Multi-objective design optimisation of ball bearings using a modified particle swarm optimisation technique

Authors: V. Savsani, R.V. Rao, D.P. Vakharia

Addresses: Department of Mechanical Engineering, S.V. National Institute of Technology, Ichchanath, Surat, Gujarat – 395 007, India. ' Department of Mechanical Engineering, S.V. National Institute of Technology, Ichchanath, Surat, Gujarat – 395 007, India. ' Department of Mechanical Engineering, S.V. National Institute of Technology, Ichchanath, Surat, Gujarat – 395 007, India

Abstract: Ball bearings are widely used as important components in most of mechanical engineering applications. These bearings find wide applications in automotive, manufacturing and aeronautical industries. The problem associated with ball bearings is that the design and selection are based on different operating conditions to reach their excellent performance, long life and high reliability. This leads to the requirement of optimal design of ball bearings. Optimisation aspects of a ball bearing are presented in this paper considering three different objectives namely, dynamic capacity, static capacity and elastohydrodynamic minimum film thickness. The design parameters include mean diameter of rolling, ball diameter, number of balls and inner and outer race groove curvature radii. Different constants associated with the constraints are given some ranges and are included as design variables. The constraints considered are pertaining to the assembly angle, ball size, bearing width size, ensuring running mobility, thickness of bearing rings and curvature radii. The optimisation procedure is carried out using a modified particle swarm optimisation (PSO) technique. Both single and multi-objective optimisation aspects are considered. The results of the proposed technique are compared with the previously published results. The proposed technique has given much better results in comparison to the previously tried approaches.

Keywords: ball bearing design; optimum design; multi-objective optimisation; non-dominated sorting genetic algorithms; GAs; particle swarm optimisation; PSO; inertia weight factor; ball bearings.

DOI: 10.1504/IJDE.2008.024790

International Journal of Design Engineering, 2008 Vol.1 No.4, pp.412 - 433

Published online: 30 Apr 2009 *

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