Publication:
Optimal design of differential mount using nature-inspired optimization methods

dc.contributor.authorAlbak, Emre İsa
dc.contributor.authorSolmaz, Erol
dc.contributor.authorÖztürk, Ferruh
dc.contributor.buuauthorALBAK, EMRE İSA
dc.contributor.buuauthorSOLMAZ, EROL
dc.contributor.buuauthorÖZTÜRK, FERRUH
dc.contributor.departmentHibrit ve Elektrikli Araç Teknolojisi Programı
dc.contributor.orcid0000-0001-9215-0775
dc.contributor.researcheridI-9483-2017
dc.contributor.researcheridDTV-6021-2022
dc.contributor.researcheridJHZ-3155-2023
dc.date.accessioned2024-11-18T07:11:45Z
dc.date.available2024-11-18T07:11:45Z
dc.date.issued2021-08-31
dc.description.abstractStructural performance and lightweight design are a significant challenge in the automotive industry. Optimization methods are essential tools to overcome this challenge. Recently, nature-inspired optimization methods have been widely used to find optimum design variables for the weight reduction process. The objective of this study is to investigate the best differential mount design using nature-based optimum design techniques for weight reduction. The performances of the nature-based algorithms are tested using convergence speed, solution quality, and robustness to find the best design outlines. In order to examine the structural performance of the differential mount, static analyses are performed using the finite element method. In the first step of the optimization study, a sampling space is generated by the Latin hypercube sampling method. Then the radial basis function metamodeling technique is used to create the surrogate models. Finally, differential mount optimization is performed by using genetic algorithms (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), ant lion optimizer (ALO) and dragonfly algorithm (DA), and the results are compared. All methods except PSO gave good and close results. Considering solution quality, robustness and convergence speed data, the best optimization methods were found to be MFO and ALO. As a result of the optimization, the differential mount weight is reduced by 14.6 wt.-% compared to the initial design.
dc.identifier.doi10.1515/mt-2021-0006
dc.identifier.eissn2195-8572
dc.identifier.endpage769
dc.identifier.issn0025-5300
dc.identifier.issue8
dc.identifier.startpage764
dc.identifier.urihttps://doi.org/10.1515/mt-2021-0006
dc.identifier.urihttps://www.degruyter.com/document/doi/10.1515/mt-2021-0006/html
dc.identifier.urihttps://hdl.handle.net/11452/47967
dc.identifier.volume63
dc.identifier.wos000803255300011
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherWalter de Gruyter Gmbh
dc.relation.journalMaterials Testing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGrey wolf
dc.subjectShape optimization
dc.subjectAnt lion
dc.subjectAlgorithm
dc.subjectCrashworthiness
dc.subjectAnt lion optimizer
dc.subjectDragonfly algorithm
dc.subjectMoth-flame optimization
dc.subjectGrey wolf optimizer
dc.subjectAluminium alloy
dc.subjectScience & technology
dc.subjectTechnology
dc.subjectMaterials science, characterization & testing
dc.subjectMaterials science
dc.titleOptimal design of differential mount using nature-inspired optimization methods
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentHibrit ve Elektrikli Araç Teknolojisi Programı
local.contributor.departmentOtomot Mühendisliği Bölümü
relation.isAuthorOfPublicationd966c82c-3610-4ddf-9d0a-af656d61472a
relation.isAuthorOfPublicationc1b21677-a248-47ee-baba-2f53cfeb7f50
relation.isAuthorOfPublication407521cf-c5bd-4b05-afca-6412ef47700b
relation.isAuthorOfPublication.latestForDiscoveryd966c82c-3610-4ddf-9d0a-af656d61472a

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