Optimization Design of Gear Train Based on Particle Swarm Optimization Algorithm

Chang Wei Wu; Yong Hai Wu; Cong Bin Ma; Cheng Wang

doi:10.4028/www.scientific.net/AMM.373-375.1072

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 373-375Optimization Design of Gear Train Based on...

Optimization Design of Gear Train Based on Particle Swarm Optimization Algorithm

Abstract:

Particle swarm optimization algorithms have lots of advantages such as fast convergence speed, good quality of solution and robustness in multidimensional space function optimization and dynamic target optimization. It is suitable for structural optimization design. In this paper, manual transmission gear train of a tractor is taken as research object, the minimum quality and minimum center distance of the gear train is taken as optimization goal, the gear ratio, modulus, helix angle, tooth width and equilibrium conditions of the axial force are taken as the constraints, a multi-objective optimization model of the gear train is established. The optimal structure design programs and Pareto optimal solution are obtained by using particle swarm optimization algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 373-375)

Pages:

1072-1075

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.373-375.1072

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Online since:

August 2013

Authors:

Chang Wei Wu, Yong Hai Wu, Cong Bin Ma, Cheng Wang

Keywords:

Gear Train, Multi-Objective Optimization Design, Particle Swarm Optimization (PSO)

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References

[1] T. Peter, K. Andeas. Local Parameters Particle Swarm Optimization, Proceedings Hybrid Intelligent Systems, HIS'06. Sixth International Conference, 2006, pp.52-54.

DOI: 10.1109/his.2006.264935

Google Scholar

[2] X. Cui, T. Potok, E. Palathingal. Clustering using Particle Swarm Optimization, Proceedings IEEE, 2005, pp.185-191.

DOI: 10.1109/sis.2005.1501621

Google Scholar

[3] L. Dasheng, K.C. Tan C.K. Goh.A. Particle Swarm Algorithm for Multiobjective Design Optimization,. IEEE Transactions on systems Man and Cybernetics, 2007, Vol 37, pp.42-50.

DOI: 10.1109/tsmcb.2006.883270

Google Scholar

[4] Parsopoulos KE, Vrahatis MN. Particle swarm optimization method in multi-objective problems. Proceedings of the 2002 ACM Symposium on Applied Computing . (2002).

DOI: 10.1145/508791.508907

Google Scholar

[5] Kennedy, and Eberhart, R C. Particle Swarm Optimization. Proc. of the IEEE the International Conference on Neural Network . (1995).

Google Scholar

[6] Eberhart R C, Shi Y. Particle swarm optimization: developments, applications and resources. Proc, Congress on Evolutionary Computation (2010).

DOI: 10.1109/cec.2001.934374

Google Scholar