| 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 |
Distilling Thermal Signatures from Reduced Order Physics Models for Electron Beam Powder Bed Fusion Processing |
| Author(s) |
Patxi Fernandez-Zelaia, Sebastien Dryepondt, Amir Koushyar Ziabari, Michael M. Kirka |
| On-Site Speaker (Planned) |
Patxi Fernandez-Zelaia |
| Abstract Scope |
Extracting meaningful process-structure trends from spatiotemporal thermal data simulation can be extremely challenging. Model parameter uncertainty, simplified physics, and idealized conditions, introduced model bias which further makes difficult the quantification of salient thermal features. In this work we propose an approach to encode the spatiotemporal outputs from a reduced order thermal model into a compact latent space representation. This is achieved using a self-supervised video-transformer machine learning framework. The identified latent representation summarizes the relevant physics and, given sufficient data, allows for calculation of similarity measures. As an example the approach is used to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. This methodology is well suited to be used towards in-situ process monitoring, scan pattern design, and component qualification. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |