Pragmatic generative optimization of novel structural lattice metamaterials with machine learning
Metamaterials, otherwise known as architected or programmable materials, enable designers to tailor mesoscale topology and shape to achieve unique material properties that are not present in nature. Additionally, with the recent proliferation of additive manufacturing tools across industrial sectors...
Main Authors: | Anthony P. Garland, Benjamin C. White, Scott C. Jensen, Brad L. Boyce |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-05-01
|
Series: | Materials & Design |
Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127521001854 |
Similar Items
-
Optimal Design of the Band Structure for Beam Lattice Metamaterials
by: Andrea Bacigalupo, et al.
Published: (2019-01-01) -
Anisotropic dissipation in lattice metamaterials
by: Dimitri Krattiger, et al.
Published: (2016-12-01) -
Inverse machine learning framework for optimizing lightweight metamaterials
by: Adithya Challapalli, et al.
Published: (2021-10-01) -
Surface Lattice Resonances in THz Metamaterials
by: Thomas CaiWei Tan, et al.
Published: (2019-06-01) -
Towards unusual mechanical properties of tensegrity lattice metamaterial
by: Al Sabouni-Zawadzka Anna, et al.
Published: (2018-01-01)