Multi-objective spur gear design using teaching learning-based optimization and decision-making techniques
The optimization of gears is crucial to the development of energy efficient mechanical systems. Weight, volume and power output are major objectives dependent on reduced inertia of rotary, mobile systems and losses in power transmission. In the present work, an extended version of an optimal weight...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Taylor & Francis Group
2019
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/edacecbf9616475da6f43a873a13e3cf |
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| Sumario: | The optimization of gears is crucial to the development of energy efficient mechanical systems. Weight, volume and power output are major objectives dependent on reduced inertia of rotary, mobile systems and losses in power transmission. In the present work, an extended version of an optimal weight design problem available in literature is investigated using multi-objective teaching and learning-based optimization (MOTLBO). Four design cases differentiated by variable ranges and sets were formulated based on an optimal weight design problem in literature. Power input and contact ratio variables were added to the design problem formulation which was investigated by previous authors as a single objective minimum weight problem. The generated Pareto frontiers were also investigated using decision-making methods viz. Linear Programming for Multidimensional Analysis of Preference (LINMAP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Results obtained reflect the trade-off effects of multiple objectives by increase in optimal weight value as compared to previous studies. The results also highlight the importance of design preference articulation by reflecting on minimum possible results which were better than some obtained in literature. |
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