Optimization Design of 3Z(Ⅱ) Planetary Gear Train based on Parallel Selected Genetic Algorithm

The optimization design method of 3Z( Ⅱ) planetary gear train is studied. The multiple object optimization model,whose objective functions are the minimum of volume of gear train and the maximum of transmission efficiency is built. The constrained optimization is transformed into unconstrained optim...

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Main Authors: Zhang Qiang, Liu Zefeng
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Transmission 2017-01-01
Series:Jixie chuandong
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Online Access:http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.01.032
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author Zhang Qiang
Liu Zefeng
author_facet Zhang Qiang
Liu Zefeng
author_sort Zhang Qiang
collection DOAJ
description The optimization design method of 3Z( Ⅱ) planetary gear train is studied. The multiple object optimization model,whose objective functions are the minimum of volume of gear train and the maximum of transmission efficiency is built. The constrained optimization is transformed into unconstrained optimization by penalty function,and a solution algorithm is proposed based on parallelism selection genetic algorithm( GA).The case shows,the proposed method can apply to the optimization methods of 3Z( Ⅱ) planetary gear train and the optimization effects are noticeable. There is much for reference of this method to other optimization of planetary gear train.
format Article
id doaj-art-007262b0a6f341cf8fd8ddf4ef7b85ad
institution Kabale University
issn 1004-2539
language zho
publishDate 2017-01-01
publisher Editorial Office of Journal of Mechanical Transmission
record_format Article
series Jixie chuandong
spelling doaj-art-007262b0a6f341cf8fd8ddf4ef7b85ad2025-01-10T14:39:15ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392017-01-014115115429928403Optimization Design of 3Z(Ⅱ) Planetary Gear Train based on Parallel Selected Genetic AlgorithmZhang QiangLiu ZefengThe optimization design method of 3Z( Ⅱ) planetary gear train is studied. The multiple object optimization model,whose objective functions are the minimum of volume of gear train and the maximum of transmission efficiency is built. The constrained optimization is transformed into unconstrained optimization by penalty function,and a solution algorithm is proposed based on parallelism selection genetic algorithm( GA).The case shows,the proposed method can apply to the optimization methods of 3Z( Ⅱ) planetary gear train and the optimization effects are noticeable. There is much for reference of this method to other optimization of planetary gear train.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.01.0323Z (Ⅱ) planetary gear trainMultiple object optimizationPenalty functionParallel selected genetic algorithm
spellingShingle Zhang Qiang
Liu Zefeng
Optimization Design of 3Z(Ⅱ) Planetary Gear Train based on Parallel Selected Genetic Algorithm
Jixie chuandong
3Z (Ⅱ) planetary gear train
Multiple object optimization
Penalty function
Parallel selected genetic algorithm
title Optimization Design of 3Z(Ⅱ) Planetary Gear Train based on Parallel Selected Genetic Algorithm
title_full Optimization Design of 3Z(Ⅱ) Planetary Gear Train based on Parallel Selected Genetic Algorithm
title_fullStr Optimization Design of 3Z(Ⅱ) Planetary Gear Train based on Parallel Selected Genetic Algorithm
title_full_unstemmed Optimization Design of 3Z(Ⅱ) Planetary Gear Train based on Parallel Selected Genetic Algorithm
title_short Optimization Design of 3Z(Ⅱ) Planetary Gear Train based on Parallel Selected Genetic Algorithm
title_sort optimization design of 3z ii planetary gear train based on parallel selected genetic algorithm
topic 3Z (Ⅱ) planetary gear train
Multiple object optimization
Penalty function
Parallel selected genetic algorithm
url http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.01.032
work_keys_str_mv AT zhangqiang optimizationdesignof3ziiplanetarygeartrainbasedonparallelselectedgeneticalgorithm
AT liuzefeng optimizationdesignof3ziiplanetarygeartrainbasedonparallelselectedgeneticalgorithm