Data-Driven Midsole (2020)
article⁄Data-Driven Midsole (2020)
abstract⁄With the advancement of additive manufacturing, computational approaches are gaining popularity in midsole design. We develop an experimental understanding of the midsole as a field and develop designs that are informed by running data. We streamline two data types, namely underfoot pressure and surface deformation, to generate designs. Unlike typical approaches in which certain types of lattices get distributed across the midsole according to average pressure data, we use ARAMIS data, reflecting the distinct surface deformation characteristics, as our primary design driver. We analyze both pressure and deformation data temporally, and temporal data patterns help us generate and explore a design space to search for optimal designs. First, we define multiple zones across the midsole space using ARAMIS data clustering. Then we develop ways to blend and distribute auxetic and isosurface lattices across the midsole. We hybridize these two structures and blend datadetermined zones to enhance visual continuity while applying FEA simulations to ensure structural integrity. This multiobjective optimization approach helps enhance the midsole’s structural performance and visual coherence while introducing a novel approach to 3Dprinted footwear design.
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| Year |
2020 |
| Authors |
Tian, Runjia; Wang, Yujie; Gun, Onur Yuce. |
| Issue |
ACADIA 2020: Distributed Proximities / Volume I: Technical Papers |
| Pages |
188-197. |
| Library link |
N/A |
| Entry filename |
data-driven-midsole |
