Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning
Virginia Tech · University of California, Los Angeles · +4 more institutions
Abstract
Designing and printing metamaterials with customizable architectures enables the realization of unprecedented mechanical behaviors that transcend those of their constituent materials. These behaviors are recorded in the form of response curves, with stress-strain curves describing their quasi-static footprint. However, existing inverse design approaches are yet matured to capture the full desired behaviors due to challenges stemmed from multiple design objectives, nonlinear behavior, and process-dependent manufacturing errors. Here, we report a rapid inverse design methodology, leveraging generative machine learning and desktop additive manufacturing, which enables the creation of nearly all possible uniaxial…
Citation impact
- FWCI
- 39.06
- Percentile
- 100%
- References
- 43
Authors
11Topics & keywords
- Computer science
- Inverse
- Realization (probability)
- Process (computing)
- Metamaterial
- Design process
- Engineering design process
- Generative model