Kinetostatic Benchmark of Rear Suspension Systems for Motorcycle
Abstract
This paper provides a benchmark for motorcycle rear suspension systems. The main goal is to determine whether any of the suspension systems provides clear advantages over the others when seeking for a previously defined progressive wheel rate. A kinetostatic formulation of the mechanism is therefore presented. In this formulation, kinematics is based on groups of elements, while statics is based on the principle of virtual work. This formulation has been proved to be efficient and robust. It allows for building objective functions which are especially suitable for evolutionary algorithm optimization. Results show that there are no significant differences between the four types of analysed suspensions.
Keywords
motorcycle rear suspension dimensional synthesis progressiveness groups of elementsPreview
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References
- 1.Castillo, J.J., Giner, P., Simón, A., Cabrera, J.A.: Optimal design of motorcycle rear suspension systems using genetic algorithms. In: Mechanisms and Machine Science 7, New Trends in Mechanism and Machine Science, Theory and Applications in Engineering, pp. 181–189. Springer (2012)Google Scholar
- 2.Fernández de Bustos, I., Aguirrebeitia, J., Avilés, R., López, D.: Optimización dimensional de mecanismos incluyendo objetivos de velocidad. In: Proceedings of XVIII Congreso Nacional de Ingeniería Mecánica (CNIM 2010), Ciudad Real, Spain (2010)Google Scholar
- 3.Foale, T.: Motocicletas, Comportamiento dinámico y diseño de chasis. Ed. Tony Foale Designs (2003)Google Scholar
- 4.Noriega, A., Cadenas, M., Fernández, R.: Position problems in Assur’s groups with revolute pairs. In: Mechanisms and Machine Science 7, New trends in Mechanism and Machine Science, Theory and Applications in Engineering, pp. 141–148. Springer (2012)Google Scholar
- 5.Noriega, A., Moreda, Y., Sierra, J.-M.: Síntesis cineto-estática de una suspensión delantera alternativa para una motocicleta. In: Proceedings of XIX Congreso Nacional de Ingeniería Mecánica (CNIM 2012), Castellón, Spain (2012)Google Scholar
- 6.Noriega, A., et al.: An efficient optimization method to obtain the set of most promising minima in multimodal problems. Int. J. for Simulation and Multidisciplinary Optimization 3-4, 424–431 (2009)CrossRefGoogle Scholar