Optimización de transmisiones de engranajes mediante algoritmos evolutivos
[EN] This paper shows a genetic algorithm (GA)-based optimization procedure for gear trains design. Gear design uses simultaneous discrete (P.E. pitch) and continuous variables nonlinearly related. However, unlike GAs, most optimization methods are only suited for continuous design variables. This p...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2013 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/36305 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/36305 |
| Access Level: | acceso abierto |
| Palavra-chave: | Optimización Transmisión Engranaje Algoritmos genéticos Genetic Algorithm Optimization Gear train INGENIERIA DE LOS PROCESOS DE FABRICACION INGENIERIA MECANICA |
| Resumo: | [EN] This paper shows a genetic algorithm (GA)-based optimization procedure for gear trains design. Gear design uses simultaneous discrete (P.E. pitch) and continuous variables nonlinearly related. However, unlike GAs, most optimization methods are only suited for continuous design variables. This paper uses GAs as a tool to achieve not only the optimal design, but also a series of near-optimal designs. To achieve this objective, first the optimization problem is formulated. It must be multiobjective (maximum strength, minimum energetic losses, etc) and restricted. A mechanism to transform the constrained problem into unconstrained thought penalty functions is proposed. Recommendations on the objective function and penalty terms are also suggested. Next a design variables coding and decoding method, as well the genetic operators of reproduction, crossover and mutation are presented. Finally, it is analyzed an example in which the developed genetic algorithm has been used, comparing the obtained results from a previous optimization. |
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