Encoding materials and data for iterative personalization

\u3cp\u3eData is changing how we design consumer products. Shoe production is a prime example of this; foot size, footstep pressure and personal preferences can be used to design personalized shoes. Research done around metamaterials, programming materials and computational composites illustrate the possibilities of creating complex data & material relationships. These new relationships allow us to look at future products almost like software apps, becoming a kind of product service systems, where the focus is on its iterative personalized improvement over time. Can we create systems of such data driven objects that in turn allow us to design new objects that are informed by the data trail? In this paper we report on four RtD project iterations that explore this challenge and provide a set of insights on how to close this new iterative loop.\u3c/p\u3

Towards ultra personalized 4D printed shoes

\u3cp\u3eIn this case study three designers supported by multiple stakeholders created a pair of fully personalized printed high heel shoes in a period of two months for a single user. The shoes are made with soft and flexible materials for dynamic fit and use. The shoes are not only uniquely formed to the user’s feet but the geometry of the material is designed to support and flex with the movement of each foot. These shoes utilize a 4D printing approach in the way they are made to fit the user while they move and change. Designing a shoe to such a degree represents a form of Ultra Personalization. This case study of an ultra personalized approach addresses the negotiation of key design considerations: aesthetics, comfort, robustness, balance and temperature. The findings inform digital fabrication design, software, and tools for designers.\u3c/p\u3