| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Accelerating Design and Additive Manufacturing of Polymer Matrix Composites |
| Author(s) |
Olivia Fulkerson, Akash Deep, Srikanthan Ramesh, Hadi Noori, Erik Inman |
| On-Site Speaker (Planned) |
Olivia Fulkerson |
| Abstract Scope |
Polymer matrix composites (PMCs) offer exceptional mechanical performance and low weight, making them ideal for various applications. 3D printing enables the efficient production of functional composite parts with customized mechanical properties. However, the current optimization process for 3D printing PMCs involves trial-and-error, which is time-consuming and costly. To address this, a Bayesian optimization (BO) framework is proposed in this project to accelerate the design and production of high-strength, low-weight 3D printed PMCs. The BO framework models the 3D printing process as a black-box function using minimal experimental data. A probabilistic model is developed to recommend the next set of experiments iteratively until the optimized process parameters are reached. Our results demonstrate that the proposed method can efficiently find the global optima for black-box functions, such as 3D printing. This research has potential to benefit the additive manufacturing industry by providing a scalable approach that can accelerate the process workflow. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |