Optimal design of transmission shafts: A continuous genetic algorithm approach
This 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 re...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9181
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9181
- Palabra clave:
- Genetic algorithm
Mechanical design
Non-linear equations
Optimization�
Shaft design
Simulation
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Optimal design of transmission shafts: A continuous genetic algorithm approach |
| title |
Optimal design of transmission shafts: A continuous genetic algorithm approach |
| spellingShingle |
Optimal design of transmission shafts: A continuous genetic algorithm approach Genetic algorithm Mechanical design Non-linear equations Optimization� Shaft design Simulation |
| title_short |
Optimal design of transmission shafts: A continuous genetic algorithm approach |
| title_full |
Optimal design of transmission shafts: A continuous genetic algorithm approach |
| title_fullStr |
Optimal design of transmission shafts: A continuous genetic algorithm approach |
| title_full_unstemmed |
Optimal design of transmission shafts: A continuous genetic algorithm approach |
| title_sort |
Optimal design of transmission shafts: A continuous genetic algorithm approach |
| dc.subject.keywords.none.fl_str_mv |
Genetic algorithm Mechanical design Non-linear equations Optimization� Shaft design Simulation |
| topic |
Genetic algorithm Mechanical design Non-linear equations Optimization� Shaft design Simulation |
| description |
This 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. |
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2019 |
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2019 |
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2020-03-26T16:33:09Z |
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2020-03-26T16:33:09Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/article |
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Artículo |
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Statistics, Optimization and Information Computing; Vol. 7, Núm. 4; pp. 802-815 |
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2311004X |
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https://hdl.handle.net/20.500.12585/9181 |
| dc.identifier.doi.none.fl_str_mv |
10.19139/soic-2310-5070-641 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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57208634458 55791991200 57212511520 56919564100 |
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Statistics, Optimization and Information Computing; Vol. 7, Núm. 4; pp. 802-815 2311004X 10.19139/soic-2310-5070-641 Universidad Tecnológica de Bolívar Repositorio UTB 57208634458 55791991200 57212511520 56919564100 |
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https://hdl.handle.net/20.500.12585/9181 |
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eng |
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eng |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial 4.0 Internacional |
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openAccess |
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Recurso electrónico |
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application/pdf |
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International Academic Press |
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International Academic Press |
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2020-03-26T16:33:09Z2020-03-26T16:33:09Z2019Statistics, Optimization and Information Computing; Vol. 7, Núm. 4; pp. 802-8152311004Xhttps://hdl.handle.net/20.500.12585/918110.19139/soic-2310-5070-641Universidad Tecnológica de BolívarRepositorio UTB57208634458557919912005721251152056919564100This 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.Recurso electrónicoapplication/pdfengInternational Academic Presshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076918825&doi=10.19139%2fsoic-2310-5070-641&partnerID=40&md5=b9f1135d1c1f1c64575dd0ed19b55a11Optimal design of transmission shafts: A continuous genetic algorithm approachinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Genetic algorithmMechanical designNon-linear equationsOptimization�Shaft designSimulationRodriguez-Cabal M.A.Grisales-Noreña, Luis FernandoArdila Maŕn J.Montoya O.D.Mott, R.L., Machine elements in mechanical design. (1991)Alguliyev, R.M., Aliguliyev, R.M., Abdullayeva, F.J., ""PSO+K-means Algorithm for Anomaly Detection in Big Data,"" Statistics,Optimization and Information Computing (2019), 7 (2), pp. 348-359Elanchezhian, C., Vijaya Ramnath, B., Sripada Raghavendra, K.N., Muralidharan, M., Rekha, G., Design and Comparison of the Strength and Efficiency of Drive Shaft made of Steel and Composite Materials. (2018) Materials Today: Proceedings, 5 (1), pp. 1000-1007. , https://doi.org/10.1016/j.matpr.2017.11.176Reddy, K., Nagaraju, C., Weight optimization and Finite Element Analysis of Composite automotive drive shaft for Maximum Stiffness,Materials Today: Proceedings (2017), 4 (2), pp. 2390-2396. , https://doi.org/10.1016/j.matpr.2017.02.088Koechlin, S., Dehmani, H., Kulcsár, G., Strength of a pinion-motor shaft connection: Computational and experimental assessment. (2017) Procedia Engineering, 213, pp. 477-487. , https://doi.org/10.1016/j.proeng.2018.02.047Grisales-Noreña, L.F., Diseño Y Operación De Sistemas De Distribución Bajo Un Ambiente De Redes Inteligentes. (2015)Garcia, Á., Tecnicas metaheurísticas (2013) UPMGuedria, N., Improved accelerated PSO algorithm for mechanical engineering optimization problems, Applied Soft Computing Journal , 40, pp. 455-467. , https://doi.org/10.1016/j.asoc.2015.10.048Husseinzadeh Kashan, A., An efficient algorithm for constrained global optimization and application to mechanical engineering design (2011) League championship algorithm (LCA), CAD Computer Aided Design, 43 (12), pp. 1769-1792. , https://doi.org/10.1016/j.cad.2011.07.003De Melo, V., Carosio, G.L., Investigating Multi-View Differential Evolution for solving constrained engineering design problems. (2013) Expert Systems with Applications, 40 (9), pp. 3370-3377. , https://doi.org/10.1016/j.eswa.2012.12.045Lampinen, J., Cam shape optimisation by genetic algorithm. (2003) CAD Computer Aided Design, 35 (8), pp. 727-737. , https://doi.org/10.1016/S0010-4485(03)00004-6Abdelsalam, A.M., El-Shorbagy, M.A., Optimization of wind turbines siting in a wind farm using genetic algorithm based local search. (2018) Renewable Energy, 123, pp. 748-755. , Https://Doi.Org/10.1016/J.Renene.2018.02.083urlGallego, R.A., Escobar, A.H., Toro, E.M., Tecnicas metaheurísticas de optimización, (2nd ed.). (2008) Pereira: Universidad Tecnológica de Pereira.Mastorakis, N.E., Solving Non-linear Equations via Genetic Algorithms. (2005) Proceedings of the 6th WSEAS International Conference on Evolutionary Computing, 2005, pp. 24-28Norton, R.L., Disẽo de máquinas (1st ed.). (1999) Prentice HallSchaeffler, K.G., Rodamientos FAG. (2009)Hibbeler, R.C., Mecánica de materiales (Sexta Edic). (2006) Mexico: Prentice Hall.Giri, 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 Transactions on Circuits and Systems, 7 (5), pp. 416-427Guzmán, M.A., Delgado, A., Optimización de la geometría de un eje aplicando algoritmos genéticos.Ingeniería e Investigación (2005), 25 (2), pp. 15-23Gebze, K., Genetic Algorithm based Feature Selection in High Dimensional Text Dataset Classification (2015), 12AISI 1040 Steel, cold drawn. (2018), http://www.matweb.com/search/DataSheet.aspx?MatGUID=39ca4b70ec2844b888d999e3753be83a&ckck=1Beer, F.P., Jhonston, E.R.J., Mecanica Vectorial Para Ingenieros ""Estatica"" (6th ed.). (1997) McGRAW-HILL.Comino, P., Carigliano, S., Free Beam Calculator. (2013), https://skyciv.com/es/free-beam-calculator/Souza, S.S.F., Romero, R., Pereira, J., Saraiva, J.T., Specialized Genetic Algorithm of Chu-Beasley Applied Considering Several Demand Scenarios, (2015) IEEE Eindhoven PowerTechSingh, N., Dhir, V., ""Hypercube Based Genetic Algorithm for Efficient VM Migration for Energy Reduction in Cloud Computing,""Statistics, Optimization and Information Computing (2019), 7, pp. 468-485http://purl.org/coar/resource_type/c_6501THUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9181/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9181oai:repositorio.utb.edu.co:20.500.12585/91812023-05-26 11:15:27.412Repositorio Institucional UTBrepositorioutb@utb.edu.co |