Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms

The problem of minimum weight design of simple and multi-stage spur gear trains has been a subject of considerable interest, since many high-performance power transmission applications (e.g., automotive, aerospace, machine tools, etc.) require low weight. This paper presents two advanced optimizatio...

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Veröffentlicht in:Mechanism and machine theory Jg. 45; H. 3; S. 531 - 541
Hauptverfasser: Savsani, V., Rao, R.V., Vakharia, D.P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.03.2010
Elsevier
Schlagworte:
Applied sciences
Drives
Evolutionary computation
Exact sciences and technology
Gear train
Gears
Genetic algorithm
Mechanical engineering. Machine design
Optimal weight design
Particle swarm optimization
Simulated annealing
Optimal weight design
Genetic algorithm
Particle swarm optimization
Gear train
Evolutionary computation
Simulated annealing
High performance
Motor car
Evolutionary algorithm
Power transmission
Machining
Multistage apparatus
Neural network
Modeling
Weight
Optimal design
Engineering design
High power
Machine tool
Aerospace
Swarm intelligence
Spur gear
ISSN:0094-114X, 1873-3999
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Beschreibung
Zusammenfassung:The problem of minimum weight design of simple and multi-stage spur gear trains has been a subject of considerable interest, since many high-performance power transmission applications (e.g., automotive, aerospace, machine tools, etc.) require low weight. This paper presents two advanced optimization algorithms known as particle swarm optimization (PSO) and simulated annealing (SA) to find the optimal combination of design parameters for minimum weight of a spur gear train. The results of the proposed algorithms are compared with the previously published results. It is observed that the proposed algorithms offer better gear design solutions.
ISSN:0094-114X
1873-3999
DOI:10.1016/j.mechmachtheory.2009.10.010