About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
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Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
Presentation Title |
Using Generative Adversarial Networks for the Design of Metamaterials to Reach New Property Spaces |
Author(s) |
Chandra Veer Singh, Sahar Choukir |
On-Site Speaker (Planned) |
Chandra Veer Singh |
Abstract Scope |
Recent advances in additive manufacturing and machine learning are ushering in a new age of data-driven material design. Guided by bio-inspiration, experimentation and systematic optimization, a number of metamaterials have been synthesized with mechanical properties reaching theoretical stiffness limits. However, experimental design of such materials remains challenging. Here, we present an approach for the design of metamaterials with optimal stiffness using machine learning approaches via generative adversial networks. Finite elements models were carried on millions of randomly generated 3D architectures based on different crystallographic symmetries to extract young’s moduli, shear moduli and bulk moduli. The data served to train the networks and identify hundreds of new metamaterials designs at the unit-cell level with our target mechanical property: optimal stiffness. The significance of the approach lies in the development of a ML-based platform that allows computers to design novel 3D isotropic metamaterials achieving the Hashin-Shtrikman upper bound with no prior knowledge. |