Design optimization of deep groove ball bearings using crowding distance particle swarm optimization

被引：0
作者：
Aparna Duggirala
R K Jana
R Venkat Shesu
Prasun Bhattacharjee
机构：
[1] Maulana Abul Kalam Azad University of Technology,Department of Industrial Engineering and Management
[2] Indian Institute of Management Raipur,Department of Mechanical Engineering
[3] GEC Campus,undefined
[4] Indian National Centre for Ocean Information Services,undefined
[5] Ministry of Earth Sciences,undefined
[6] Jadavpur University,undefined
来源：
Sādhanā | 2018年 / 43卷
关键词：
Mechanical design; design optimization; deep groove ball bearings; multi-objective optimization; particle swarm optimization; crowding distance;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper presents a crowding distance particle swarm optimization technique to optimize the design parameters of deep groove ball bearings. The design optimization problem is multi-objective in nature. The considered objectives are maximizing dynamic and static load bearing capacities and minimizing elasto-hydrodynamic film thickness. The technique is applied to bearings used in transmission system of a tractor for the purpose of farming. Pareto optimal solutions are obtained using the proposed technique. The results obtained from the technique are found to be superior compared with NSGA-II and catalogue values.
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[1]  
Makhecha N(2015)Optimization of dynamic load carrying capacity of deep groove ball bearing using teaching–learning based optimization technique Int. J. Sci. Res. Dev. 3 2321-2613
[2]  
Patel RC(1972)A Survey of optimization of mechanical design J. Eng. Ind. 94 495-499
[3]  
Seireg A(2013)Meta-heuristics for manufacturing scheduling and logistics problems Int. J. Prod. Econ. 141 1-3
[4]  
Jong C(2003)Rolling element bearing design through Genetic Algorithms Eng. Optim. 35 649-659
[5]  
Gen M(2007)Multi-objective design optimization of rolling bearings using genetic algorithms Mech. Mach. Theory 42 1418-1443
[6]  
Tiwari MK(2011)Optimum design of rolling element bearings using genetic algorithm – differential evolution hybrid algorithm J. Mech. Eng. Sci. 225 714-721
[7]  
Chang PC(2007)Optimum design of rolling element bearings using genetic algorithms Mech. Mach. Theory 42 233-250
[8]  
Chakraborty I(2013)Novel inertial weight strategies for particle swarm optimization Memetic Comput. 5 229-251
[9]  
Kumar V(2016)The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks Transp. Res. Part E: Logist. Transp. Rev. 91 208-226
[10]  
Nair SB(2016)Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization Computers & Industrial Engineering 96 201-215
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