| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
|
| Presentation Title |
SimuLearn: Machine Learning-empowered Fast and Accurate Simulator to Support 4D Printing Design |
| Author(s) |
Humphrey Yang, Kuanren Qian, Haolin Liu, Yuxuan Yu, Jianzhe Gu, Matthew McGehee, Yongjie Jessica Zhang, Lining Yao |
| On-Site Speaker (Planned) |
Humphrey Yang |
| Abstract Scope |
Recent technological advancements have created a library of smart materials that afford novel functionalities. 4D printing, in particular, administers more efficient and economical prototyping as well as manufacturing. However, due to the lack of fast and accurate transformation simulators, currently available 4D printing CAD tools cannot effectively support users to iterate designs that have complex topologies. To address this issue, we take mesh-like structures as an example to introduce a novel SimuLearn system that combines finite element analysis (FEA) and graph convolutional networks (GCN) to truthfully (97% accuracy versus FEA) inform design decisions in real-time (0.6 seconds) and deploy our implementation in a computational design tool to unveil the enabled design space. Results show that SimuLearn enables much faster design iteration and allows users to integrate material response into their design workflows. Beyond 4D printing, SimuLearn also enriches the computational toolbox for designing, engineering, and predicting smart, transformative materials. |