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Thomas Fromentèze1,2, Philippe Michaud3, Ali Hassny4

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Summary
This summary is machine-generated.

This study introduces a decentralized generative model for autonomous mechanical structure growth, enabling precise control over local stiffness and anisotropy for advanced engineering applications.

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Area of Science:

  • Engineering
  • Materials Science
  • Computational Mechanics

Background:

  • Living organisms exhibit complex functional structures through spontaneous development.
  • Engineering design seeks novel strategies inspired by biological morphogenesis.
  • Current methods like topology optimization can be computationally intensive.

Purpose of the Study:

  • To develop a decentralized generative model for autonomous growth of mechanical structures.
  • To achieve controlled tensorial properties, specifically tailored stiffness and anisotropy.
  • To bypass iterative global solves common in traditional optimization techniques.

Main Methods:

  • Adapting Turing's reaction-diffusion concept with anisotropic diffusion.
  • Utilizing a database linking morphogenetic parameters to effective elastic tensors via homogenization.
  • Implementing a decentralized generative model for local microstructure control.

Main Results:

  • Successfully grew mechanical structures with tailored local stiffness and anisotropy.
  • Achieved target orthotropic tensors without adjoint or topology optimization.
  • Demonstrated independent control over local anisotropy and rigidity.

Conclusions:

  • The proposed morphogenetic model enables autonomous growth of complex mechanical structures.
  • This approach offers local control over material properties, circumventing limitations of global optimization.
  • Experimental validation, including mechanical cloaking, confirms the method's efficacy.