Multi-objective spur gear design using teaching learning-based optimization and decision-making techniques

The optimization of gears is crucial to the development of energy efficient mechanical systems. Weight, volume and power output are major objectives dependent on reduced inertia of rotary, mobile systems and losses in power transmission. In the present work, an extended version of an optimal weight...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Edmund S. Maputi, Rajesh Arora
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2019
Materias:	optimization design gear motlbo multi-objective Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/edacecbf9616475da6f43a873a13e3cf
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:edacecbf9616475da6f43a873a13e3cf
record_format	dspace
spelling	oai:doaj.org-article:edacecbf9616475da6f43a873a13e3cf2021-11-04T15:51:56ZMulti-objective spur gear design using teaching learning-based optimization and decision-making techniques2331-191610.1080/23311916.2019.1665396https://doaj.org/article/edacecbf9616475da6f43a873a13e3cf2019-01-01T00:00:00Zhttp://dx.doi.org/10.1080/23311916.2019.1665396https://doaj.org/toc/2331-1916The optimization of gears is crucial to the development of energy efficient mechanical systems. Weight, volume and power output are major objectives dependent on reduced inertia of rotary, mobile systems and losses in power transmission. In the present work, an extended version of an optimal weight design problem available in literature is investigated using multi-objective teaching and learning-based optimization (MOTLBO). Four design cases differentiated by variable ranges and sets were formulated based on an optimal weight design problem in literature. Power input and contact ratio variables were added to the design problem formulation which was investigated by previous authors as a single objective minimum weight problem. The generated Pareto frontiers were also investigated using decision-making methods viz. Linear Programming for Multidimensional Analysis of Preference (LINMAP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Results obtained reflect the trade-off effects of multiple objectives by increase in optimal weight value as compared to previous studies. The results also highlight the importance of design preference articulation by reflecting on minimum possible results which were better than some obtained in literature.Edmund S. MaputiRajesh AroraTaylor & Francis Grouparticleoptimizationdesigngearmotlbomulti-objectiveEngineering (General). Civil engineering (General)TA1-2040ENCogent Engineering, Vol 6, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	optimization design gear motlbo multi-objective Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	optimization design gear motlbo multi-objective Engineering (General). Civil engineering (General) TA1-2040 Edmund S. Maputi Rajesh Arora Multi-objective spur gear design using teaching learning-based optimization and decision-making techniques
description	The optimization of gears is crucial to the development of energy efficient mechanical systems. Weight, volume and power output are major objectives dependent on reduced inertia of rotary, mobile systems and losses in power transmission. In the present work, an extended version of an optimal weight design problem available in literature is investigated using multi-objective teaching and learning-based optimization (MOTLBO). Four design cases differentiated by variable ranges and sets were formulated based on an optimal weight design problem in literature. Power input and contact ratio variables were added to the design problem formulation which was investigated by previous authors as a single objective minimum weight problem. The generated Pareto frontiers were also investigated using decision-making methods viz. Linear Programming for Multidimensional Analysis of Preference (LINMAP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Results obtained reflect the trade-off effects of multiple objectives by increase in optimal weight value as compared to previous studies. The results also highlight the importance of design preference articulation by reflecting on minimum possible results which were better than some obtained in literature.
format	article
author	Edmund S. Maputi Rajesh Arora
author_facet	Edmund S. Maputi Rajesh Arora
author_sort	Edmund S. Maputi
title	Multi-objective spur gear design using teaching learning-based optimization and decision-making techniques
title_short	Multi-objective spur gear design using teaching learning-based optimization and decision-making techniques
title_full	Multi-objective spur gear design using teaching learning-based optimization and decision-making techniques
title_fullStr	Multi-objective spur gear design using teaching learning-based optimization and decision-making techniques
title_full_unstemmed	Multi-objective spur gear design using teaching learning-based optimization and decision-making techniques
title_sort	multi-objective spur gear design using teaching learning-based optimization and decision-making techniques
publisher	Taylor & Francis Group
publishDate	2019
url	https://doaj.org/article/edacecbf9616475da6f43a873a13e3cf
work_keys_str_mv	AT edmundsmaputi multiobjectivespurgeardesignusingteachinglearningbasedoptimizationanddecisionmakingtechniques AT rajesharora multiobjectivespurgeardesignusingteachinglearningbasedoptimizationanddecisionmakingtechniques
_version_	1718444685469417472

Multi-objective spur gear design using teaching learning-based optimization and decision-making techniques

Ejemplares similares