Designing architected materials for mechanical compression via simulation, deep learning, and experimentation
Year: 2023
Type: article
Abstract: Architected materials can achieve enhanced properties compared to their plain counterparts. Specific architecting serves as a powerful design lever to achieve targeted behavior without changing the base material. Thus, the connection between architected structure and resultant properties remains an open field of great interest to many fields, from aerospace to civil to automotive applications. Here, we focus on properties related to mechanical compression, and design hierarchical honeycomb structures to meet specific values of stiffness and compressive stress. To do so, we employ a combination of techniques in a singular workflow, starting with molecular dynamics simulation of the forward design problem, augmenting with data-driven artificial intelligence models to address the inverse design problem, and verifying the behavior of de novo structures with experimentation of additively manufactured samples. We thereby demonstrate an approach for architected design that is generalizable to multiple material properties and agnostic to the identity of the base material.
Source: npj Computational Materials
Authors: Andrew J. Lew, Kai Jin, Markus J. Buehler
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,190
Funders: National Science Foundation, National Institutes of Health, Multidisciplinary University Research Initiative, Quest for Intelligence, Massachusetts Institute of Technology, Office of Naval Research, National Institute of Arthritis and Musculoskeletal and Skin Diseases, Air Force Office of Scientific Research, Army Research Office
OpenAlex ID: W4378384209