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

Abstract

As scientists relentlessly seek advancements in the design of function-driven materials for societal benefits such as efficiency and sustainability, generative design of mechanical metamaterials - materials whose properties are primarily defined by internal structures rather than chemical compositions, has attracted significant interest from multi-disciplinary research communities. In this dissertation, 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 in applications across engineering domains, from wearable devices and soft robotics to compliant architected materials. The creation of textiles has a multiscale nature, as 1D yarns are interlooped into 2D geometries with topological invariants, and then finally assembled into 3D fabrics. The immense versatility that makes textiles attractive, through iteration of materials at the 1D level and structures at the 2D level, also poses the challenge of predicting their mechanical behaviors as 3D devices, due to design selections made at different length scales. This has created a demand for mechanical and statistical models to investigate the behaviors of textiles across scales, in order to provide physical insights guiding the design of textiles. Meanwhile, the rise of big data has called for the development of suitable parameterization that can complement systematic inverse design techniques for optimized functionalities. Overall, I adopt a bottom-up paradigm to probe how topology and geometry affect the elasticity of textiles, with first-principle modeling based on 1D yarns as rods. I first detail the generalized description, modeling, and simulation in Chapter 2. Then in Chapter 3, I establish an interdisciplinary paradigm to study weft-knitted fabrics, which are typical building blocks of textile-like metamaterials for their distinctive extensibility without material damage and spatially programmable anisotropy. Using experiments and micromechanically-based simulations, I uncover the roles of yarn dynamics on the nonlinear elasticity of weft-knitted fabrics under uniaxial tension along principal fabric directions. Following that, in Chapter 4, I utilize the validated computational model to further explore the general elastic energy landscapes of weft-knitted fabrics under varying uniaxial and biaxial loading conditions, in order to provide more physical insights into the origin of their nonlinear behavior from macroscopic perspectives. With the learned physical insights on how topology and geometry affect the elasticity of textiles, I propose a yarn-based parameterization for generative design of topologically programmable textiles in Chapter 5. Finally, I draw inspiration from flexible mechanical metamaterials to discuss how to integrate data-driven techniques into the design of textile-like metamaterials and provide future perspectives in Chapter 6.