| 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 |
Accurate In-situ Detection of Localized Porosity in LPBF Using Infrared Camera |
| Author(s) |
Berkay Bostan, Shawn Hinnebusch , David Anderson , Albert To |
| On-Site Speaker (Planned) |
Berkay Bostan |
| Abstract Scope |
Porosity in Laser Powder Bed Fusion (LPBF) parts significantly impacts durability and strength that are crucial in safety-critical applications. Addressing this challenge, precise porosity control is essential for the reliability of LPBF-manufactured components. Previous studies on LPBF porosity prediction often used inappropriate process parameters to simplify detection, typically focusing on larger, more noticeable pores. This study diverges from such methods by developing a machine learning framework that precisely predicts localized porosity. Utilizing various features from in-situ infrared camera monitoring, the framework was tested on entirely unseen parts and achieved over 90% accuracy while maintaining a false positive ratio of less than 3%, even for pores ten times smaller than the sensor resolution. Furthermore, SHAP (SHapley Additive exPlanations) analysis was employed to investigate pore formation mechanisms, revealing complex interplays in different regimes. This research not only advances in-situ porosity detection in LPBF but also deepens the understanding of pore formation mechanisms. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |