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Automatic design of mechanical metamaterial actuators.


ABSTRACT: Mechanical metamaterial actuators achieve pre-determined input-output operations exploiting architectural features encoded within a single 3D printed element, thus removing the need for assembling different structural components. Despite the rapid progress in the field, there is still a need for efficient strategies to optimize metamaterial design for a variety of functions. We present a computational method for the automatic design of mechanical metamaterial actuators that combines a reinforced Monte Carlo method with discrete element simulations. 3D printing of selected mechanical metamaterial actuators shows that the machine-generated structures can reach high efficiency, exceeding human-designed structures. We also show that it is possible to design efficient actuators by training a deep neural network which is then able to predict the efficiency from the image of a structure and to identify its functional regions. The elementary actuators devised here can be combined to produce metamaterial machines of arbitrary complexity for countless engineering applications.

SUBMITTER: Bonfanti S 

PROVIDER: S-EPMC7441157 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Automatic design of mechanical metamaterial actuators.

Bonfanti Silvia S   Guerra Roberto R   Font-Clos Francesc F   Rayneau-Kirkhope Daniel D   Zapperi Stefano S  

Nature communications 20200820 1


Mechanical metamaterial actuators achieve pre-determined input-output operations exploiting architectural features encoded within a single 3D printed element, thus removing the need for assembling different structural components. Despite the rapid progress in the field, there is still a need for efficient strategies to optimize metamaterial design for a variety of functions. We present a computational method for the automatic design of mechanical metamaterial actuators that combines a reinforced  ...[more]

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