Optimal Design of Transmission Shafts: a Continuous Genetic Algorithm Approach

  • Miguel Angel Rodriguez Cabal Instituto Tecnológico Metropolitano
  • Luis Fernando Grisales Noreña Instituto Tecnológico Metropolitano
  • Juan Gonzalo Ardila Marín Instituto Tecnológico Metropolitano Instituto Técnico Industrial Pascual Bravo
  • Oscar Danilo Montoya Giraldo Universidad Tecnológica de Bolívar
Keywords: Genetic Algorithm, Shaft Design, Mechanical Design, Simulation, Non-linear Equations, Optimization.

Abstract

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.Theproposedoptimizationmodel corresponds 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.

Author Biographies

Miguel Angel Rodriguez Cabal, Instituto Tecnológico Metropolitano
 Mecatronica y Electromecanica departmentResearch group Materiales Avanzados y Energia (MatyEr)Estudiante de ingenieria
Luis Fernando Grisales Noreña, Instituto Tecnológico Metropolitano
Mecatronica y Electromecanica departmentResearch group Materiales Avanzados y Energia (MatyEr)Professor 
Juan Gonzalo Ardila Marín, Instituto Tecnológico Metropolitano Instituto Técnico Industrial Pascual Bravo
Mecatronica y Electromecanica departmentResearch group Materiales Avanzados y Energia (MatyEr)Professor Department of Mecánica y afinesGrupo de investigación e innovación en energía (GIIEN)
Oscar Danilo Montoya Giraldo, Universidad Tecnológica de Bolívar
Grupo de Investigación de Automatización Industrial y Control (GAICO)Professor

References

Mott RL (1991) Machine elements in mechanical design.

R.M.Alguliyev,R.M.Aliguliyev,and F.J.Abdullayeva,“PSO+K-means Algorithm for Anomaly Detection in BigData,”Stat.Optim. Inf. Comput., vol. 7, no. 2, pp. 348–359, 2019, doi.org/10.19139/soic.v7i2.623

Elanchezhian C, Vijaya Ramnath B., Sripada Raghavendra KN, Muralidharan M & Rekha G. (2018) Design and Comparison of the Strength and Efficiency of Drive Shaft made of Steel and Composite Materials. Materials Today: Proceedings 5(1), 1000-1007.https://doi.org/10.1016/j.matpr.2017.11.176

Reddy K, Nagaraju C (2017) Weight optimization and Finite Element Analysis of Composite automotive drive shaft for Maximum StiffnessMaterials Today: Proceedings 4(2), 2390–2396. https://doi.org/10.1016/j.matpr.2017.02.088

Kœchlin S, Dehmani H, & Kulcs´ar G (2017) Strength of a pinion-motor shaft connection: Computational and experimental assessment. Procedia Engineering, 213(2017), 477–487. https://doi.org/10.1016/j.proeng.2018.02.047

Grisales-Nore˜na LF (2015) Dise˜no Y Operaci´on De Sistemas De Distribuci´on Bajo Un Ambiente De Redes Inteligentes.

Garcia ´A (2013) T´ecnicas metaheur´ısticas. UPM

GuedriaN Improved accelerated PSO algorithm for mechanical engineering optimization problems Applied Soft Computing Journal, 40, 455–467.https://doi.org/10.1016/j.asoc.2015.10.048

Husseinzadeh Kashan A (2011) An efficient algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA)CAD Computer Aided Design, 43(12), 1769-1792.

https://doi.org/10.1016/j.cad.2011.07.003

De Melo V & Carosio GL (2013) Investigating Multi-View Differential Evolution for solving constrained engineering design problems. Expert Systems with Applications, 40(9), 3370–3377. https://doi.org/10.1016/j.eswa.2012.12.045

Lampinen, J. (2003). Cam shape optimisation by genetic algorithm. CAD Computer Aided Design, 35(8 SPEC.), 727–737. https://doi.org/10.1016/S0010-4485(03)00004-6

Abdelsalam AM.&El-ShorbagyMA(2018).Optimization of wind turbines siting in a wind farm using genetic algorithm based local search. Renewable Energy, 123, 748–755. https://doi.org/10.1016/j.renene.2018.02.083

Gallego RA, Escobar AH & Toro EM (2008). T´ecnicas metaheur´ısticas de optimizaci´on (2nd ed.). Pereira: Universidad Tecnol´ogica de Pereira.

Mastorakis, N. E. (2005). Solving Non-linear Equations via Genetic Algorithms. Proceedings of the 6th WSEAS International Conference on Evolutionary Computing, 2005, 24–28.

Norton, R. L. (1999). Dise˜no de m´aquinas (1st ed.). Prentice Hall. 16. Schaeffler KG. (2009). Rodamientos FAG.

Hibbeler RC (2006).Mec´anica de materiales (Sexta Edic). Mexico: Prentice Hall.

GiriC,TipparthiDKR & ChattopadhyayS(2008).A genetic algorithm based approach for system-on-chip test scheduling using dual speed TAM with power constraint. WSEAS Transactions on Circuits and Systems, 7(5), 416–427.

Guzm´anMA & DelgadoA(2005).Optimizaci´ondelageometr´ıadeuneje aplicandoalgoritmosgen´eticos.Ingenier´ıaeInvestigaci´on, 25(2), 15–23.

Gebze K (2015). Genetic Algorithm based Feature Selection in High Dimensional Text Dataset Classification, 12.

MatWeb. (2018). AISI 1040 Steel, cold drawn. Retrieved from http://www.matweb.com/search/DataSheet.aspx?MatGUID= 39ca4b70ec2844b888d999e3753be83a&ckck=1

Beer FP & Jhonston ERJ (1997). Mecanica Vectorial Para Ingenieros “Estatica” (6th ed.). McGRAW-HILL.

Comino P & Carigliano S. (2013). Free Beam Calculator. Retrieved from https://skyciv.com/es/free-beam-calculator/

Souza SSF, Romero R, Pereira J & Saraiva JT (2015). Specialized Genetic Algorithm of Chu-Beasley Applied Considering Several Demand Scenarios IEEE Eindhoven PowerTech, doi.org/10.1109/PTC.2015.7232298

N. Singh and V. Dhir, “Hypercube Based Genetic Algorithm for Efficient VM Migration for Energy Reduction in Cloud Computing,” vol. 7, no. June, pp. 468–485, 2019, doi.org/10.19139/soic.v7i2.541

Published
2019-12-01
How to Cite
Rodriguez Cabal, M. A., Grisales Noreña, L. F., Ardila Marín, J. G., & Montoya Giraldo, O. D. (2019). Optimal Design of Transmission Shafts: a Continuous Genetic Algorithm Approach. Statistics, Optimization & Information Computing, 7(4), 802-815. https://doi.org/10.19139/soic-2310-5070-641
Section
Research Articles