Towards Generative Design for Functionality: Topology, Geometry, and Elasticity of Textiles

As scientists relentlessly seek advancements in the design of function-driven materials for societal benefits such as efficiency and sustainability, the generative design of mechanical metamaterials has attracted significant interest from multidisciplinary research communities. In this talk, I focus on the study of textiles, an everyday object as well as an emerging material system with an analogy to mechanical metamaterials, to embed human-centered functionalities, such as compliance in wide applications. The creation of textiles has a multiscale nature, as 1D yarns are interloped into 2D geometries with topological invariants, and then finally assembled into 3D fabrics. The emergent elasticity and immense versatility that make textiles attractive also pose the challenge of predicting their mechanical behavior. We built a dynamical model to study the evolution of micromechanical states of textiles under varying load conditions, in order to capture their nonlinearity and anisotropy. The richness exhibited in their energy landscapes, which can be manipulated for desirable functionality, also calls for a systematic sampling and learning framework. To extend our previous work studying representative knitted fabrics, we propose a generalized parameterization to reconstruct 3D fibrous networks from 2D representative structures, assembled with varying local curvature and topology based on a yarn representation. This is the first step to enhance the data availability of textile-based metamaterials beyond conventional design and eventually towards generative design for functional textiles.