Enabled by developments in the additive manufacturing of engineered systems with unprecedented levels of compositional and structural complexity, there have been major advances in architected materials that possess exciting mechanical properties dictated by their engineered structure. Within the realm of dynamics, the idea of using engineered structures to produce novel properties has been historically focused primarily on linear waves. This work seeks to understand the propagation of non-linear waves in mechanical metamaterials with application toward control of mechanical signals in larger-amplitude loading (for example, to provide protection during impact events) and toward reconfigurable soft structures (for example, locomotion of soft robots). This research will promote interdisciplinary research and teaching. Students of all levels will learn concepts and techniques from materials science and engineering, computational and analytic mechanical modeling, advanced manufacturing, and sensing. Moreover, the research team will integrate these efforts with undergraduate research programs, outreach efforts, and educational material development at both institutions to provide research opportunities to underrepresented groups of all ages (K-12, undergraduate, and graduate).

The objective of this work is to understand the fundamental connection between geometry and the propagation of non-linear waves in compliant mechanical metamaterials comprising rotating polygons. Rather than limiting the scope to periodic systems as in previous work, the geometry of the metamaterials will be locally tailored, and inhomogeneities will be harnessed to achieve target dynamic responses such as maximal energy absorption, soliton lensing, and locomotion. The multi-faceted research program will intertwine theoretical, numerical, and experimental activities to generate new insights into the non-linear dynamic behavior of mechanical metamaterials based on rotating units. To achieve this, the research is organized into three thrusts, focusing on (1) the effect of inhomogeneities on the propagation of non-linear waves, (2) reconfigurability of system dynamics via non-linear waves, and (3) optimization of geometry and defects via machine learning to achieve targeted dynamic properties (energy absorption, steering waves, locomotion, etc.).

This project is co-funded by the Dynamics, Control, and Systems Diagnostics (DCSD) and the Mechanics of Materials and Structures (MOMS) programs in the Civil, Mechanical, and Industrial Innovation Division.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Harvard University
United States
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