Rolling element bearing design through genetic algorithms

Multi-principle preventive maintenance: a design-oriented scheduling study for mechanical systems

“…In the past, many GAs have been prescribed to solve multi-objective optimization problems (Andersson, 2001;Chakraborty et al, 2003;Qiu et al, 2014). Among different multi-objective genetic algorithms (MOGA), the strength Pareto evolutionary algorithm (SPEA2) is commonly regarded as one of the best in terms of search performance.…”

Multi-objective design optimisation of rolling bearings using genetic algorithms

“…Some contribution on the design of rolling bearings was made by earlier work of Chakraborty et al [10] and Rao and Tiwari [11], where GA was used to optimize dynamic capacity for rolling bearings. Results in [10,11] were found to be as much as 1.5 times better than those given in standard bearing catalogue [19]. Primary difference between previous works and that presented in this paper is the inclusion of multiple objectives for the simultaneous optimisation.…”

Optimal design of paired tapered roller bearings under centred radial and axial static loads

“…The work uses both deterministic methods (penalty functions) and stochastic algorithms (simulated annealing and genetic search). The genetic approach was further explored by Chakraborty et al [7] for ball bearings and its merits were compared to conventional techniques. Also based on genetic algorithms, a non-linear optimization procedure was developed by Rao and Tiwari [8] for the design of ball bearings with maximum fatigue life under kinematic constraints.…”

“…Papers dealing with the optimization of bearing features have appeared only lately, aimed at maximizing one or several performance properties of ball bearings [5][6][7][8][9][10][11], cylindrical roller bearings [12,13] and tapered roller bearings [14][15][16][17][18][19].…”