Shape optimization of a planetary gear train based on the minimum weight

Daoudi, Kaoutar; Boudi, El Mostapha

doi:10.1109/irsec.2016.7984076

Cited by 4 publications

(2 citation statements)

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“…For the number of teeth, just the number of teeth of the sun and the planet will be considered as variables. e number of teeth Z r is generated by the linear equation of Z s and Z p , equation (13). Equation (1) defines the expression of the weight function:…”

Section: Objective Functionmentioning

confidence: 99%

“…In the study of Akhila and Reddy [12], the main aim was to design and analyze a three-stage epicyclical planetary reduction gear unit used for a flight vehicle. Daoudi and Boudi [13] have proposed a methodology for the shape optimization of the epicyclical gear train system based on parametric shape optimization. eir objective was to design a lighter gearbox with respect to kinematic and dynamic behavior.…”

Section: Introductionmentioning

confidence: 99%

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Genetic Approach for Multiobjective Optimization of Epicyclical Gear Train

Daoudi

Boudi

Mohsine

2019

Mathematical Problems in Engineering

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The determination of optimal design of the planetary gear train with a lightweight, a short center distance, and a high efficiency is an important issue in the preliminary design of power transmission systems. Conventional and traditional methods have been widely used in optimization. They are deterministic and limited to solve some mechanical problems with several variables and constraints. Therefore, some optimization methods have been developed, such as the nonconventional method, the genetic algorithm (GA). This paper describes a multiobjective optimization for the epicyclical gear train system using the GA. It is aimed to obtain the optimal dimensions for epicyclical gear components like a module, number of teeth, the tooth width, the shaft diameter of the gears, and a performed efficiency under the variation of operating mode of PGT system. The problem is formulated under the satisfaction of assembly and balance constraints, bending strength, contact strength of teeth, and other dimension conditions. The mathematical model and all steps of the GA are presented in detail.

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Section: Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%