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

• Published in: Mechanism and machine theory Vol. 45; no. 3; pp. 531 - 541
• Main Authors: Savsani, V., Rao, R.V., Vakharia, D.P.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2010; Elsevier
• Subjects: 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
• DOI: 10.1016/j.mechmachtheory.2009.10.010

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.