Optimization of a drive shaft using PSO algorithm

Mechanical design involves several continuous variables associated with the calculation of elements that compose the parts implemented in different processes. However, when the values associated with several design variables are selected, the range of each such variable may result in infinite soluti...

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Tipo de recurso:
Fecha de publicación:
2018
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/8905
Acceso en línea:
https://hdl.handle.net/20.500.12585/8905
Palabra clave:
ANSYS® simulation
Drive shaft
Machinery design
Particle swarm optimization
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restrictedAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/8905
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.none.fl_str_mv Optimization of a drive shaft using PSO algorithm
title Optimization of a drive shaft using PSO algorithm
spellingShingle Optimization of a drive shaft using PSO algorithm
ANSYS® simulation
Drive shaft
Machinery design
Particle swarm optimization
title_short Optimization of a drive shaft using PSO algorithm
title_full Optimization of a drive shaft using PSO algorithm
title_fullStr Optimization of a drive shaft using PSO algorithm
title_full_unstemmed Optimization of a drive shaft using PSO algorithm
title_sort Optimization of a drive shaft using PSO algorithm
dc.subject.keywords.none.fl_str_mv ANSYS® simulation
Drive shaft
Machinery design
Particle swarm optimization
topic ANSYS® simulation
Drive shaft
Machinery design
Particle swarm optimization
description Mechanical design involves several continuous variables associated with the calculation of elements that compose the parts implemented in different processes. However, when the values associated with several design variables are selected, the range of each such variable may result in infinite solutions or oversized solution spaces. Thus, the choice and fit of different variables related to the mechanical parts under analysis pose a challenge to designers. This is the case of drive shaft design: the variables that represent the diameters of several transversal sections of each of its elements directly affect its weight and resistance to mechanical stresses. Therefore, the selection of variables should not be at random. This article presents the optimization of the design of a drive shaft composed of three transversal sections using the metaheuristic technique particle swarm optimization (PSO). Such problem is solved to obtain an optimal and reliable part. For that purpose, a nonlinear mathematical model was developed to represent this problem as a function of the physical features of the mechanical system. The objective function is the reduction of the weight of the shaft and the variables are the diameters of each section. The set of constraints in this problem considers the general equation to design a fatigue-safe shaft as well as a constructive constraint to establish the minimum step distance for coupling the mechanical elements. Due to the nonlinearity of the mathematical model, this work proposes PSO as optimization technique. This algorithm has proven to be an efficient tool to solve continuous nonlinear problems. Finally, the solution provided by the optimization technique is validated in ANSYS® software, thus demonstrating that the answer meets all the design criteria previously selected. © 2018, World Scientific and Engineering Academy and Society. All rights reserved.
publishDate 2018
dc.date.issued.none.fl_str_mv 2018
dc.date.accessioned.none.fl_str_mv 2020-03-26T16:32:35Z
dc.date.available.none.fl_str_mv 2020-03-26T16:32:35Z
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dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.type.hasVersion.none.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.spa.none.fl_str_mv Artículo
status_str publishedVersion
dc.identifier.citation.none.fl_str_mv WSEAS Transactions on Applied and Theoretical Mechanics; Vol. 13, pp. 130-139
dc.identifier.issn.none.fl_str_mv 19918747
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/8905
dc.identifier.instname.none.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.none.fl_str_mv Repositorio UTB
dc.identifier.orcid.none.fl_str_mv 57208634458
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identifier_str_mv WSEAS Transactions on Applied and Theoretical Mechanics; Vol. 13, pp. 130-139
19918747
Universidad Tecnológica de Bolívar
Repositorio UTB
57208634458
57200559940
55791991200
56919564100
57201332551
url https://hdl.handle.net/20.500.12585/8905
dc.language.iso.none.fl_str_mv eng
language eng
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dc.rights.cc.none.fl_str_mv Atribución-NoComercial 4.0 Internacional
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dc.format.medium.none.fl_str_mv Recurso electrónico
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dc.publisher.none.fl_str_mv World Scientific and Engineering Academy and Society
publisher.none.fl_str_mv World Scientific and Engineering Academy and Society
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spelling 2020-03-26T16:32:35Z2020-03-26T16:32:35Z2018WSEAS Transactions on Applied and Theoretical Mechanics; Vol. 13, pp. 130-13919918747https://hdl.handle.net/20.500.12585/8905Universidad Tecnológica de BolívarRepositorio UTB5720863445857200559940557919912005691956410057201332551Mechanical design involves several continuous variables associated with the calculation of elements that compose the parts implemented in different processes. However, when the values associated with several design variables are selected, the range of each such variable may result in infinite solutions or oversized solution spaces. Thus, the choice and fit of different variables related to the mechanical parts under analysis pose a challenge to designers. This is the case of drive shaft design: the variables that represent the diameters of several transversal sections of each of its elements directly affect its weight and resistance to mechanical stresses. Therefore, the selection of variables should not be at random. This article presents the optimization of the design of a drive shaft composed of three transversal sections using the metaheuristic technique particle swarm optimization (PSO). Such problem is solved to obtain an optimal and reliable part. For that purpose, a nonlinear mathematical model was developed to represent this problem as a function of the physical features of the mechanical system. The objective function is the reduction of the weight of the shaft and the variables are the diameters of each section. The set of constraints in this problem considers the general equation to design a fatigue-safe shaft as well as a constructive constraint to establish the minimum step distance for coupling the mechanical elements. Due to the nonlinearity of the mathematical model, this work proposes PSO as optimization technique. This algorithm has proven to be an efficient tool to solve continuous nonlinear problems. Finally, the solution provided by the optimization technique is validated in ANSYS® software, thus demonstrating that the answer meets all the design criteria previously selected. © 2018, World Scientific and Engineering Academy and Society. All rights reserved.Recurso electrónicoapplication/pdfengWorld Scientific and Engineering Academy and Societyhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85061290405&partnerID=40&md5=fffa2485f19d6e8619ccd1f664361041Optimization of a drive shaft using PSO algorithminfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1ANSYS® simulationDrive shaftMachinery designParticle swarm optimizationRodriguez-Cabal M.A.Marín J.A.Grisales-Noreña L.F.Montoya O.D.Del Rio J.A.S.Bhaumik, S.K., Rangaraju, R., Parameswara, M.A., Venkataswamy, M.A., Bhaskaran, T.A., Krishnan, R.V., Fatigue failure of a hollow power transmission shaft (2002) Eng. Fail. Anal, 9 (4), pp. 457-467Mott, R.L., (2004) Machine Elements in Mechanical DesignCerón, A.M., Charry, G.A., Coronado, J.J., (2006) Análisis De Falla Del Eje De Un Agitador Para Tratamiento De agua,”, (30), pp. 185-190Momčilović, D., Odanović, Z., Mitrović, R., Atanasovska, I., Vuherer, T., Failure analysis of hydraulic turbine shaft (2012) Eng. Fail. Anal, 20, pp. 54-66Harle, N., Brown, J., Rashidy, M., A feasibility study for an optimising algorithm to guide car structure design under side impact loading (1999) Int. J. Crashworthiness, 4 (1), pp. 71-92He, Q., Wang, L., An effective co-evolutionary particle swarm optimization for constrained engineering design problems (2007) Eng. Appl. Artif. Intell, 20 (1), pp. 89-99Shi, X., Chen, H., Particle swarm optimization for constrained circular-arc/line-segment fitting of discrete data points (2018) Int. J. Model. Simul, 38 (1), pp. 25-37. , JanMastorakis, N.E., Solving Non-linear Equations via Genetic Algorithms (2005) Proc. 6Th WSEAS Int. Conf. Evol. Comput., 2005, pp. 24-28Giri, C., Tipparthi, D.K.R., Chattopadhyay, S., A genetic algorithm based approach for system-on-chip test scheduling using dual speed TAM with power constraint (2008) WSEAS Trans. Circuits Syst, 7 (5), pp. 416-427Gallego, R.A., Escobar, A.H., Toro, E.M., (2008) Técnicas metaheurísticas De optimización, , 2nd ed. Pereira: Universidad Tecnológica de PereiraLampinen, J., Cam shape optimisation by genetic algorithm (2003) CAD Comput. Aided Des, 35 (8), pp. 727-737Ait Chikh, M.A., Belaidi, I., Khelladi, S., Paris, J., Deligant, M., Bakir, F., Efficiency of Bio-and Socio-inspired Optimization Algorithms for Axial Turbomachinery Design (2017) Appl. Soft Comput.Hanafi, I., Cabrera, F.M., Dimane, F., Manzanares, J.T., Application of Particle Swarm Optimization for Optimizing the Process Parameters in Turning of PEEK CF30 Composites,” (2016) Procedia Technol, 22, pp. 195-202. , October 2015Husseinzadeh Kashan, A., An efficient algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA) (2011) CAD Comput. Aided Des, 43 (12), pp. 1769-1792de Melo, V.V., Carosio, G.L.C., Investigating Multi-View Differential Evolution for solving constrained engineering design problems (2013) Expert Syst. Appl, 40 (9), pp. 3370-3377Ben Guedria, N., Improved accelerated PSO algorithm for mechanical engineering optimization problems (2016) Appl. Soft Comput. J, 40, pp. 455-467Norton, R.L., (1999) Diseño De máquinas, , 1st ed. Prentice HallKennedy, J., Eberhart, R., Particle swarm optimization (1995) Neural Networks, 4, pp. 1942-1948. , Proceedings., IEEE Int. ConfJaramillo Velez, J.F., Noreña Grisales, L.F., (2013) Sintonización Del D-Statcom Por Medio Del método De optimización PsoGuzmán, M.A., Delgado, A., Optimización de la geometría de un eje aplicando algoritmos genéticos (2005) Ing. E Investig, 25 (2), pp. 15-23(2018) AISI 1040 Steel, Cold Drawn, , http://www.matweb.com/search/DataSheet.aspx?MatGUID=39ca4b70ec2844b888d999e3753be83a&ckck=1, OnlineSchaeffler, K.G., (2009) Rodamientos FAGBeer, F.P., Jhonston, E.R.J., (1997) Mecanica Vectorial Para Ingenieros “Estatica, , 6th ed. McGRAW-HILLhttp://purl.org/coar/resource_type/c_6501THUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8905/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8905oai:repositorio.utb.edu.co:20.500.12585/89052021-02-02 13:50:51.626Repositorio Institucional UTBrepositorioutb@utb.edu.co