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

Paper Number: 100111

100111 - Programming Magneto-Mechanical Responses Into Structures via Topology Optimization 

Hard-magnetic soft materials, which consist of soft matrix embedded with hard-magnetic particles, have attracted tremendous interests owing to their untethered control capability, rapid response, wireless actuation mechanism, and flexible programmability. This work introduces a powerful topology optimization framework to guide the rational design of hard-magnetic soft materials and structures with precisely programmable functionalities under large deformations. We first propose a design parameterization scheme that systematically represents the distribution of material in the matrix phase (which determines topology), the remnant magnetization distribution, and the applied magnetic field using three sets of design variables. In particular, the remnant magnetization vector at each location of the design is interpolated from a set of pre-defined candidate vectors and is promoted to converge toward one (and only one) of the candidate vectors at the end of the optimization. We then introduce a scheme to interpolate the Helmholtz free energy function from the three sets of design variables. The interpolated Helmholtz free energy function characterizes the nonlinear response of a given design under the applied magnetic field. Built upon the unified design parameterization and interpolation schemes, the proposed framework is capable of simultaneously optimizing topology, remnant magnetization distribution, and applied magnetic fields. Thus, guided by the analytical gradient information, our framework can effectively explore the entire design space to search for optimized structures with multiple target functionalities, such as programmable deformations and maximized actuation, under the corresponding optimized magnetic fields. Through five design examples, we showcase applications of the proposed framework in generating optimized shape-programming metastructures and robots, magnetic actuators, and unit cells with encoded and adaptable modes. We demonstrate how simultaneous optimization in topology, magnetization distribution, and applied magnetic field can greatly improve the performance of a design, and highlight the importance of accounting for finite-rotation kinematics to capture the influence of body torque-related magnetic force on the optimized remnant magnetization distribution. This framework is also applied to explore magnetic-responsive unit cell designs capable of achieving various programmable and several adaptable actuation modes under different magnetic fields, mimicking how materials with positive and negative Poisson’s ratios deform. Various optimized magnetic-responsive designs with comparable performances yet distinct mechanisms are discovered, showing the effectiveness of the proposed framework to generate unconventional designs with highly programmable magnetic-actuated behaviors. We envision that the proposed topology optimization framework can potentially benefit the design process in a wide spectrum of magnetic-responsive applications, such as soft robots, magnetic actuators, and programmable metamaterials.

Presenting Author: Zhi Zhao University of Illinois at Urbana-Champaign

Presenting Author Biography: Zhi Zhao is a Ph.D. student at the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. He is working on topology optimization and experimental investigation of multi-functional magneto-active materials. More broadly, he is interested in computational mechanics, metamaterials, and machine learning.

Authors:

Zhi Zhao University of Illinois at Urbana-Champaign
Xiaojia Shelly Zhang University of Illinois at Urbana-Champaign