Project description: Magneto-elastic materials facilitate features such as shape programmability, adaptive stiffness, and tunable strength, which are criti- cal for advances in structural and robotic materials. Magneto-elastic networks are commonly fabricated by employing hard magnets embedded in soft matrices to constitute a monolithic body. These architected network materials have excellent mechanical properties but damage incurred in extreme loading scenarios are permanent. To overcome this limitation, we present a novel design for elastic bars with permanent fixed dipole magnets at their ends and demonstrate their ability to self-assemble into magneto-elastic networks under random vibrations. The magneto-elastic unit configuration, most notably the orientation of end dipoles, is shown to dictate the self-assembled network topology, which can range from quasi-ordered triangular lattices to stacks or strings of particles. Network mechanics are probed with uniaxial tensile tests and design criteria for forming stable lightweight 2D networks are established. We show that these magneto-elastic networks rearrange and break gracefully at their magnetic nodes under large excitations and yet recover their original structure at moderate random excitations. Our work paves the way for structural materials that can be self- assembled and repaired on-the-fly with random vibrations, and broadens the applications of magneto-elastic soft materials.
File and script descriptions:
(1) create_initial_config_random.ipynb - Jupyter notebook to set up random systems with magneto-elastic units.
(2) create_initial_config_perfect.ipynb - Jupyter notebook to set up perfect (ordered) systems with magneto-elastic units and remove rigid body motions after equilibration run.
(3) equil_unix_random.py - python scripts for performing equilibration run and uniaxial pulling simulations for random networks.
(4) equil_biaxial_perfect.py - python scripts for performing equilibration run and biaxial pulling simulations for perfect networks.
(5) plot_frame_animation.ipynb - save frames or gif files for the simulation.
(6) network data files: origin_net_info-xxx
(7) py_packages: necessary py functions.
Publication: Yang, X., Leng, J., Sun, C., and Keten, S. (accepted). ”Self-assembled Robust 2D Networks from Magneto-elastic Bars.” Adv. Mater. Technol.