Multi-objective optimization of two-stage helical gear train using NSGA-II
type: Item
instance of: scholarly article
title: Multi-objective optimization of two-stage helical gear train using NSGA-II
zbMATH Open document ID: 1460.90216
DOI: 10.1155/2014/670297
author: R. C. Sanghvi
author: A. S. Vashi
author: H. P. Patolia
author: R. G. Jivani
published in: Journal of Optimization
publication date: 2019-02-18T00:00:00Z
review text: Summary: Gears not only transmit the motion and power satisfactorily but also can do so with uniform motion. The design of gears requires an iterative approach to optimize the design parameters that take care of kinematics aspects as well as strength aspects. Moreover, the choice of materials available for gears is limited. Owing to the complex combinations of the above facts, manual design of gears is complicated and time consuming. In this paper, the volume and load carrying capacity are optimized. Three different methodologies (i) MATLAB optimization toolbox, (ii) genetic algorithm (GA), and (iii) multiobjective optimization (NSGA-II) technique are used to solve the problem. In the first two methods, volume is minimized in the first step and then the load carrying capacities of both shafts are calculated. In the third method, the problem is treated as a multiobjective problem. For the optimization purpose, face width, module, and number of teeth are taken as design variables. Constraints are imposed on bending strength, surface fatigue strength, and interference. It is apparent from the comparison of results that the result obtained by NSGA-II is more superior than the results obtained by other methods in terms of both objectives.
Mathematics Subject Classification ID: 90C90
Mathematics Subject Classification ID: 90C29
zbMATH DE Number: 7024757
describes a project that uses: Matlab
MaRDI profile type: Publication
Wikidata QID: Q59060808
full work available at URL: https://doi.org/10.1155/2014/670297
OpenAlex ID: W2146958349
cites work: Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms
cites work: Tradeoff analysis in minimum volume design of multi-stage spur gear reduction units
cites work: Q4079017
cites work: Q5502711
description: scientific article; zbMATH DE number 7024757