Available at: https://digitalcommons.calpoly.edu/theses/1708
Date of Award
MS in Computer Science
Creating a bicycle with a rideable geometry is more complicated than it may appear, with today’s mainstay designs having evolved through years of iteration. This slow evolution coupled with the bicycle’s intricate mechanical system has lead most builders to base their new geometries off of previous work rather than expand into new design spaces. This crutch can lead to slow bicycle iteration rates, often causing bicycles to all look about the same. To combat this, several bicycle design models have been created over the years, with each attempting to define a bicycle’s handling characteristics given its physical geometry. However, these models often analyze a single bicycle at a time, and as such, using them in an iterative design process can be cumbersome. This work seeks to improve an existing model used by the Cal Poly Mechanical Engineering department such that it can be used in a proactive, iterative fashion (as opposed to the reactive, single-design paradigm that it currently supports). This is accomplished by expanding the model’s inputs to include more bicycle components as well as differently sized riders. This augmented model is then incorporated into several search platforms ranging from a brute-force implementation to several variants using genetic algorithm concepts. These models allow the designer to specify a bicycle design search space as well as a set of riders upfront, from which the algorithms search out and find strong candidate designs to return to the user. This in turn reduces the overhead on the designer while also potentially discovering new bicycle designs which had not been considered previously viable. Finally, a front-end was created to make it easier for the user to access these algorithms and their results.