| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Symposium
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2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Presentation Title |
Melt Pool Depth Contour Prediction from Surface Thermal Images with Transformer Models |
| Author(s) |
Odinakachukwu Francis Ogoke, Peter Pak, Alexander Myers, Guadalupe Quirarte, Jack Beuth, Jonathan Malen, Amir Barati Farimani |
| On-Site Speaker (Planned) |
Odinakachukwu Francis Ogoke |
| Abstract Scope |
The use of Laser Powder Bed Fusion in certain applications is limited due to defect-induced variation in mechanical and fatigue performance. However, most methods for defect characterization take place post-build, requiring significant material and time expenses to fully characterize changes in build conditions. The three-dimensional geometry of the melt pool can serve as a real-time indicator of defect formation, as non-overlapping melt pools lead to lack-of-fusion porosity, and deep, narrow melt pools indicate keyhole porosity formation. However, the sub-surface appearance of the melt pool is not visible through in-situ monitoring methods feasible to implement during the build process. Therefore, we create a deep learning framework for predicting the melt pool sub-surface morphology from in-situ high-speed surface thermal images. We evaluate model performance by comparing the geometric properties of the predicted melt pool boundary with optical micrographs of the corresponding physical melt pool cross-section in both single-track and multi-track melting scenarios. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |