dc.creatorRodriguez-Cabal M.A.
dc.creatorGrisales-Noreña, Luis Fernando
dc.creatorArdila Maŕn J.
dc.creatorMontoya O.D.
dc.date.accessioned2020-03-26T16:33:09Z
dc.date.available2020-03-26T16:33:09Z
dc.date.created2020-03-26T16:33:09Z
dc.date.issued2019
dc.identifierStatistics, Optimization and Information Computing; Vol. 7, Núm. 4; pp. 802-815
dc.identifier2311004X
dc.identifierhttps://hdl.handle.net/20.500.12585/9181
dc.identifier10.19139/soic-2310-5070-641
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio UTB
dc.identifier57208634458
dc.identifier55791991200
dc.identifier57212511520
dc.identifier56919564100
dc.description.abstractThis paper presents an analysis of the optimal design of transmission shafts by adopting the approach of a novel continuous genetic algorithm. The optimization case study is formulated as a single-objective optimization problem whose objective function is the minimization of the total weight that results from the sum of all the sections in the shaft. Additionally,mechanical stresses and constructive characteristics are considered constraints in this case. The proposed optimization modelcorresponds to a nonlinear non-convex optimization problem which is numerically solved with a continuous variant of genetic algorithms. SKYCIV®and Autodesk Inventor®were used to verify the quality and robustness of the numerical results in this paper by means of simulation tools and analysis. The results obtained demonstrates that the methodology proposed reduce the complexity and improving the results obtained in comparison to conventional mechanical design. © 2019 International Academic Press.
dc.languageeng
dc.publisherInternational Academic Press
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial 4.0 Internacional
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85076918825&doi=10.19139%2fsoic-2310-5070-641&partnerID=40&md5=b9f1135d1c1f1c64575dd0ed19b55a11
dc.titleOptimal design of transmission shafts: A continuous genetic algorithm approach


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