UC Santa Cruz

The spoked bicycle wheel is one of the most ubiquitous tensioned structures in the world and its assembly and tensioning is largely automated. Nevertheless, the algorithms employed in the tensioning process are heuristics that essentially mimic a skilled human worker. While these heuristic can yield very well-tensioned wheels, they are not efficient and occasionally do not converge, requiring manual intervention.

This work describes an in-situ empirical modeling technique of a conventional bicycle wheel employed to determine the optimal tension adjustments necessary to align the wheel in lateral and radial directions while targeting a desired uniform spoke tension. The technique allows the mean tension of the spokes to be adjusted independently from variations around the mean. Additionally, a control algorithm is developed that uses lateral feedback and predicted intermediate wheel states to bring the wheel into alignment to the desired tension in a single iteration of adjustments of the tension of the spokes. First the method is simulated on randomly tensioned wheels. Then the algorithm is demonstrated experimentally on an actual bicycle wheel.