| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Symposium
|
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Presentation Title |
Generative AI for AM Materials Optimization and Design |
| Author(s) |
Patxi Fernandez-Zelaia, Jason Mayeur, Jiahao Cheng, Guannan Zhang, Neil Zhang, Amirkoushyar Ziabari, Saket Thapliyal, Rangasayee Kannan, Peeyush Nandwana |
| On-Site Speaker (Planned) |
Patxi Fernandez-Zelaia |
| Abstract Scope |
Materials inverse design problems are essential towards advancing various technologies from fuel cells to fusion materials. Purely experimental discovery is extremely laborious; physics-based computational routes are generally limited to solving forward problems. Machine learning based generative models are well suited for data fusion and, critically, enable inverse solutions. Very recently denoising diffusion probabilistic models have exploded in popularity with a number of successful materials specific applications. Here we explore the viability these models for various tasks ranging from structural grain-scale optimization to binder jet AM composition design. These case studies demonstrate that these models are extremely flexible optimization tools capable of various constrained optimization tasks. The probabilistic nature of these models also makes them well suited for quantifying uncertainty. We envision that future materials design frameworks will make extensive use of these models as ``search'' tools bolstering the utility of experimental and computational approaches. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |