| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
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| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Data-driven Local Porosity Prediction in Laser Powder Bed Fusion via In-situ Monitoring |
| Author(s) |
Berkay Bostan, Shawn Hinnebusch, David Anderson, Albert To |
| On-Site Speaker (Planned) |
Berkay Bostan |
| Abstract Scope |
In this study, the geometry-agnostic deep learning scheme has been developed for defect detection during the laser powder bed fusion (LPBF) process. DNNs model has been trained that gives +90% accuracy with a relatively smaller dataset. Inputs to DNNs include various thermal signatures (interpass temperatures, heat intensities, and cooling rates) and spatter locations. At the same time, when making predictions, the DNNs architecture considers the features of not only the relevant pixel, but also neighboring pixels in all directions (desired order of neighbors in the current, upper, and lower layers). The potential outcomes of this study are simultaneous defect prediction during manufacturing and repairing the defects by rescanning the concerned region. Furthermore, defect formation mechanisms have been investigated by SHAP (SHapley Additive exPlanations) feature importance analysis method, and it is observed that spattering is the most dominant factor for defect formation until the melt pool reaches a certain size. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |