2023-11-15 マサチューセッツ工科大学(MIT)
◆LIRASは2つのレーザーシステムを使用し、1つは構造を素早く励起し、もう1つは振動を測定します。これにより、数百の微小構造を超高速で評価でき、メタマテリアルの動的特性を計算することが可能です。従来の物理的なテスト法ではメタマテリアルを損傷させてしまうため、LIRASは安全で信頼性が高く、実用的なメタマテリアルの発見を加速する可能性があります。
＜関連情報＞
- https://news.mit.edu/2023/new-laser-setup-probes-metamaterial-structures-ultrafast-pulses-1115
- https://www.nature.com/articles/s41586-023-06652-x
レーザー誘起振動シグネチャーによるメタマテリアルの動的診断 Dynamic diagnosis of metamaterials through laser-induced vibrational signatures
Yun Kai,Somayajulu Dhulipala,Rachel Sun,Jet Lem,Washington DeLima,Thomas Pezeril & Carlos M. Portela
Nature Published:15 November 2023
DOI:https://doi.org/10.1038/s41586-023-06652-x
Abstract
Mechanical metamaterials at the microscale exhibit exotic static properties owing to their engineered building blocks1,2,3,4, but their dynamic properties have remained substantially less explored. Their design principles can target frequency-dependent properties5,6,7 and resilience under high-strain-rate deformation8,9, making them versatile materials for applications in lightweight impact resistance10,11,12, acoustic waveguiding7,13 or vibration damping14,15. However, accessing dynamic properties at small scales has remained a challenge owing to low-throughput and destructive characterization8,16,17 or lack of existing testing protocols. Here we demonstrate a high-throughput, non-contact framework that uses MHz-wave-propagation signatures within a metamaterial to non-destructively extract dynamic linear properties, omnidirectional elastic information, damping properties and defect quantification. Using rod-like tessellations of microscopic metamaterials, we report up to 94% direction-dependent and rate-dependent dynamic stiffening at strain rates approaching 102 s−1, as well as damping properties three times higher than their constituent materials. We also show that frequency shifts in the vibrational response allow for characterization of invisible defects within the metamaterials and that selective probing allows for the construction of experimental elastic surfaces, which were previously only possible computationally. Our work provides a route for accelerated data-driven discovery of materials and microdevices for dynamic applications such as protective structures, medical ultrasound or vibration isolation.
