Ma Chunping, Chang Yilong, Wu Shuai, Zhao Ruike Renee
Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, Ohio 43210, United States.
Department of Mechanical Engineering, Stanford University, Stanford, California 94305, United States.
ACS Appl Mater Interfaces. 2022 Jul 14. doi: 10.1021/acsami.2c09052.
Metamaterials are artificially structured materials with unusual properties, such as negative Poisson's ratio, acoustic band gap, and energy absorption. However, metamaterials made of conventional materials lack tunability after fabrication. Thus, active metamaterials using magneto-mechanical actuation for untethered, fast, and reversible shape configurations are developed to tune the mechanical response and property of metamaterials. Although the magneto-mechanical metamaterials have shown promising capabilities in tunable mechanical stiffness, acoustic band gaps, and electromagnetic behaviors, the existing demonstrations rely on the forward design methods based on experience or simulations, by which the metamaterial properties are revealed only after the design. Considering the massive design space due to the material and structural programmability, a robust inverse design strategy is desired to create the magneto-mechanical metamaterials with preferred tunable properties. In this work, we develop an inverse design framework where a deep residual network replaces the conventional finite-element analysis for acceleration, realizing metamaterials with predetermined global strains under magnetic actuations. For validation, a direct-ink-writing printing method of the magnetic soft materials is adopted to fabricate the designed complex metamaterials. The deep learning-accelerated design framework opens avenues for the designs of magneto-mechanical metamaterials and other active metamaterials with target mechanical, acoustic, thermal, and electromagnetic properties.
超材料是具有异常特性的人工结构化材料，如负泊松比、声子带隙和能量吸收等。然而，由传统材料制成的超材料在制造后缺乏可调性。因此，开发了利用磁机械驱动实现无束缚、快速且可逆形状配置的有源超材料，以调节超材料的力学响应和性能。尽管磁机械超材料在可调机械刚度、声子带隙和电磁行为方面已展现出可观的能力，但现有的演示依赖基于经验或模拟的正向设计方法，通过这种方法，超材料的特性仅在设计完成后才得以揭示。考虑到由于材料和结构可编程性而存在的巨大设计空间，需要一种强大的逆向设计策略来创建具有优选可调特性的磁机械超材料。在这项工作中，我们开发了一个逆向设计框架，其中深度残差网络取代了传统的有限元分析以加速设计过程，从而实现了在磁驱动下具有预定全局应变的超材料。为了进行验证，采用了磁性软材料的直接墨水书写打印方法来制造所设计的复杂超材料。这种深度学习加速的设计框架为设计具有目标机械、声学、热学和电磁特性的磁机械超材料及其他有源超材料开辟了道路。
