Learning to self-fold at a bifurcation
arXiv:2206.08886 · doi:10.1103/PhysRevE.107.025001
Abstract
Disordered mechanical systems can deform along a network of pathways that branch and recombine at special configurations called bifurcation points. Multiple pathways are accessible from these bifurcation points; consequently, computer-aided design algorithms have been sought to achieve a specific structure of pathways at bifurcations by rationally designing the geometry and material properties of these systems. Here, we explore an alternative physical training framework in which the topology of folding pathways in a disordered sheet is changed in a desired manner due to changes in crease stiffnesses induced by prior folding. We study the quality and robustness of such training for different `learning rules', that is, different quantitative ways in which local strain changes the local folding stiffness. We experimentally demonstrate these ideas using sheets with epoxy-filled creases whose stiffnesses change due to folding before the epoxy sets. Our work shows how specific forms of plasticity in materials enable them to learn non-linear behaviors through their prior deformation history in a robust manner.
arXiv comment11 pages, 5 figures
References in corpus (26)
- Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems
- Programming Curvature using Origami Tessellations
- Combinatorial Design of Textured Mechanical Metamaterials
- Origami Multistabilty: From Single Vertices to Metasheets
- Memory formation in matter
- Nonlinear conduction via solitons in a topological mechanical insulator
- Designing allostery-inspired response in mechanical networks
- Architecture and Co-Evolution of Allosteric Materials
- Multifarious Assembly Mixtures: Systems Allowing Retrieval of Diverse Stored Structures
- Directed aging, memory and Nature's greed
- Demonstration of Decentralized, Physics-Driven Learning
- Memory from coupled instabilities in unfolded crumpled sheets
- Supervised learning in a mechanical system
- Supervised learning in physical networks: From machine learning to learning machines
- Branches of triangulated origami near the unfolded state
- Physical model of the genotype-to-phenotype map of proteins
- Periodic training of creeping solids
- Shaping dynamical folding and misfolding pathways in mechanical metamaterials
- Associative pattern recognition through macro-molecular self-assembly
- Effect of aging on the non-linear elasticity and memory formation in materials
- Topological elasticity of flexible structures
- Desynchronous Learning in a Physics-Driven Learning Network
- Learned multi-stability in mechanical networks
- Local rules for fabricating allosteric networks
- The difficulty of folding self-folding origami
- Physical learning beyond the quasistatic limit