Optimal design of transmission shafts: A continuous genetic algorithm approach
| Autor: | Juan Gonzalo Ardila Marín, Luis Fernando Grisales Noreña, Miguel Angel Rodriguez Cabal, Oscar Danilo Montoya Giraldo |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Optimal design Mathematical optimization Control and Optimization Optimization problem Computer science 01 natural sciences Constructive Mechanical design Artificial Intelligence Robustness (computer science) Genetic algorithm 0101 mathematics Optimization� 010102 general mathematics Shaft design Nonlinear system Transmission (telecommunications) Signal Processing Computer Vision and Pattern Recognition Minification Statistics Probability and Uncertainty Simulation Information Systems Non-linear equations |
| Zdroj: | Repositorio Institucional UTB Universidad Tecnológica de Bolívar instacron:Universidad Tecnológica de Bolívar |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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