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- Title
The Nonlinear Mechanical Constitutive Model and Deep Learning Method to Inverse Design Dual-Feature-Integrated Lattice Metamaterial.
- Authors
Yun Deng; Zhixin Huang; Ying Li
- Abstract
The bio-inspired lattice structure of a regularly fibrous organization is a kind of structural material with practical value in flexible bio-integrated electronics. The recently proposed dual-feature-integrated lattice structure can accurately customize the nonlinear mechanical curve of biological issues. However, it is still lacking the constitutive models to inverse design the desirable mechanical properties. Herein, a nonlinear mechanical constitutive model for the dualfeature-integrated metamaterial is established by introducing the equilibrium equation and deformation coordination conditions. The experimental and numerical results show that the proposed constitutive model can predict accurately the stress-strain curves of dual-feature-integrated lattice structure. In addition, the machine learning-whale optimization algorithm method is used to inverse design the dual-feature-integrated lattice structure, which can quickly find the target mechanical responses (chicken skin and human skin) in a board design space. The dual-feature-integrated mechanical metamaterial has a higher structural design option in comparison to the pure horseshoe and chiral metamaterial. The finding of this work contributes to the designs of lattice structures with flexibility and stretchable electronics.
- Subjects
MECHANICAL models; METAMATERIALS; OPTIMIZATION algorithms; DEEP learning; FLEXIBLE electronics; STRUCTURAL design; STRESS-strain curves
- Publication
Advanced Engineering Materials, 2024, Vol 26, Issue 10, p1
- ISSN
1438-1656
- Publication type
Article
- DOI
10.1002/adem.202302011