Validation and improvement of a bicycle crank arm based in numerical simulation and uncertainty quantification

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In this study, a finite element model of a bicycle crank arm are compared to experimental results. The structural integrity of the crank arm was analyzed in a universal dynamic test bench. The instrumentation used has allowed us to know the fatigue behavior of the component tested. For this, the prototype was instrumented with three rectangular strain gauge rosettes bonded in areas where failure was expected. With the measurements made by strain gauges and the forces registers from the load cell used, it has been possible to determine the state of the stresses for different loads and boundary conditions, which has subsequently been compared with a finite element model. The simulations show a good agreement with the experimental results, when the potential sources of uncertainties are considered in the validation process. This analysis allowed us to improve the original design, reducing its weight by 15%. The study allows us to identify the manufacturing process that requires the best metrological control to avoid premature crank failure. Finally, the numerical fatigue analysis carried out allows us to conclude that the new crank arm can satisfy the structural performance demanded by the international bicycle standard. Additionally, it can be suggested to the standard to include the verification that no permanent deformations have occurred in the crank arm during the fatigue test. It has been observed that, in some cases this bicycle component fulfils the minimum safety requirements, but presents areas with plastic strains, which if not taken into account can increase the risk of injury for the cyclist due to unexpected failure of the component.
Crank Arm, Fatigue Test, Metamodel, Statistical Validation, Strain Gauges, Uncertainty Propagation
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Gutiérrez-Moizant, R., Ramírez-Berasategui, M., Calvo, J. A., & Álvarez-Caldas, C. (2020). Validation and Improvement of a Bicycle Crank Arm Based in Numerical Simulation and Uncertainty Quantification. In Sensors (Vol. 20, Issue 7, p. 1814). MDPI AG.