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Automatic design of mechanical metamaterial actuators

Author

Listed:
  • Silvia Bonfanti

    (Department of Physics, University of Milan)

  • Roberto Guerra

    (Department of Physics, University of Milan)

  • Francesc Font-Clos

    (Department of Physics, University of Milan)

  • Daniel Rayneau-Kirkhope

    (Department of Physics, University of Milan)

  • Stefano Zapperi

    (Department of Physics, University of Milan
    Istituto di Chimica della Materia Condensata e di Tecnologie per l’Energia)

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.

Suggested Citation

  • Silvia Bonfanti & Roberto Guerra & Francesc Font-Clos & Daniel Rayneau-Kirkhope & Stefano Zapperi, 2020. "Automatic design of mechanical metamaterial actuators," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17947-2
    DOI: 10.1038/s41467-020-17947-2
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    Cited by:

    1. Francesc Font-Clos & Marco Zanchi & Stefan Hiemer & Silvia Bonfanti & Roberto Guerra & Michael Zaiser & Stefano Zapperi, 2022. "Predicting the failure of two-dimensional silica glasses," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

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