Session: 16-01-01: Poster Session: NSF-Funded Research (Grad & Undergrad)

Paper Number: 100141

100141 - Inverse Design of Magnetically Active Metasurfaces and Robots 

Inverse design of stimulus-active planar material for 3D shape morphing has been widely studied with broad applications in soft robotics, foldable structures, and biomedical devices. As one of the stimulus-active materials, hard magnetic soft materials composed of soft polymeric matrices and embedded hard magnetic particles have received considerable attention as they are capable of achieving rapid, untethered, wireless, and reversible shape shifting under magnetic actuation. These advantages make the hard magnetic soft material an excellent candidate for generating promising metasurfaces and robots that can achieve intricate yet programmable 3D large deformation. The sophisticated shape morphing is enabled through the interactions among the design geometry, the embedded hard magnetic particles, and the applied magnetic field. Therefore, it is desirable to conduct the inverse design of arbitrary 3D shapes in a programmable fashion by taking account of the complete material design space. Most of the existing designs of the hard magnetic soft materials for untethered 3D shape morphing rely on intuition-based or bio-inspired approaches, which limits the design space.

This work proposes a topology optimization framework of hard magnetic soft materials for inverse designing metasurfaces and robots that can precisely achieve target out-of-plane large deformation and can realize improved actuation performance. The design topologies, remnant magnetization distributions, and the applied magnetic fields can be optimized simultaneously. The materials are modeled in 3D using the solid finite element. The constitutive model of the ideal hard magnetic soft material is employed to characterize its magneto-mechanical behavior. The soft matrix, remnant magnetization distributions, and the applied magnetic field are parameterized by three sets of design variables, correspondingly. The Helmholtz free energy function is interpolated to capture the magneto-mechanical performance of the parameterized designs. Two objective functions are employed for (1) programming prescribed deformation modes of metasurfaces and robots and (2) maximizing the actuation performance for actuators, respectively. Three numerical designs, i.e., kirigami-inspired metasurface, bio-inspired robots, and multi-functional magnetic metasurface actuators, are presented to showcase the extensive capabilities of the proposed topology optimization inverse design paradigm in optimizing magneto-responsive planar structures with programmed deformation. The presented metasurfaces and robots demonstrate how the intricate target deformation can be programmed with high preciseness via the topology optimization inverse design approach. The optimized actuators demonstrate how the actuation performance can be improved via topology optimization. We remark that the reported design paradigm has the potential to enable a wide spectrum of magneto-responsive applications, such as magnetic metasurface actuators, wireless robotics, magnetically controllable bio-medical devices, etc.  

Presenting Author: Chao Wang University of Illinois Urbana-Champaign

Presenting Author Biography: Chao Wang is a Ph.D. student working in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign. Her research interest mainly focuses on the magnetic activated shape programming using the topology optimization approach.

Authors:

Chao Wang University of Illinois Urbana-Champaign
Zhi Zhao University of Illinois Urbana-Champaign
Xiaojia Shelly Zhang University of Illinois Urbana-Champaign