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.

Automatic design of mechanical metamaterial actuators / S. Bonfanti, R. Guerra, F. Font Clos, D. Rayneau-Kirkhope, S. Zapperi. - In: NATURE COMMUNICATIONS. - ISSN 2041-1723. - 11:1(2020 Aug 20), pp. 4162.1-4162.10.

Automatic design of mechanical metamaterial actuators

S. Bonfanti
Primo
;
R. Guerra
Secondo
;
F. Font Clos;S. Zapperi
Ultimo
2020

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.
Settore FIS/03 - Fisica della Materia
On demand design of reversible shape changing metamaterials (METADESIGN)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/759041
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