Group: MIT Design Lab
Sponsor: PUMA
Duration: 2017 – 2020
Role: Project Lead, Computational Designer (2019 – 2020)

The Metaride project began with research into auxetic structures. These unique geometric configurations have a negative Poisson’s ratio, lending them the curious tendency to shrink in all directions when compressed. As such, they have a particularly springy, responsive behavior, and they represent an exciting field of study for use in sportswear applications, such as footwear.

Multiple patterns were designed, studied, and evaluated based on their responsiveness, apparent durability, and overall aesthetics.

This study led to the formation of the recurve pattern, an adaptable pattern of figure eight-like voids that exhibit auxetic behavior. The Design Lab then began designing and simulating a running midsole with this pattern.

Having settled on an overall pattern, a computational design process was then developed that enabled us to work within key manufacturing constraints, adapt the pattern to a variety of orientations and midsole profiles, and control the overall size and aesthetic of the individual pins. These tools enabled us to quickly and visually create different patterns that encapsulated the possibilities and constraints of the auxetic system.    

FEA simulation provided the opportunity to study how the recurve pattern would perform under the loading conditions of a typical runner, and enabled the Lab to iteratively test and refine the design of the midsole.

In order to refine the simulation, an experiment was set up to record pressure data along with high-speed video footage during different athletic movements. This data was delivered in files containing over 6.5 million data. The data was then parsed into usable formats using custom Python scripts.

The simulation process made possible entirely new avenues of inquiry, as the cost to physically prototype and test an unknown technological proposal or system typically rendered such research infeasible. With simulation, designs could be studied quantitatively and qualitatively, using a variety of visualization techniques.

In addition to simulation, we occasionally created physical models of our proposed midsoles. These models helped to verify the intuitions we were building up using simulation, and occasionally provided novel insights that we wouldn’t otherwise have captured.

The final design is currently in production. As the design has moved through the actual manufacturing process, simulation has helped to verify decisions made along the way: As potentially costly changes are made to the design, they are first tested digitally, in an attempt to minimize any additional issues.

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