Designing architected materials for mechanical compression via simulation, deep learning, and experimentation
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Machine Learning in Materials Science · Cellular and Composite Structures · Block Copolymer Self-Assembly
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (41)
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