| 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 |
AI-driven In Situ Detection of Keyhole Pore Generation in Laser Powder Bed Fusion |
| Author(s) |
Zhongshu Ren, Tao Sun |
| On-Site Speaker (Planned) |
Zhongshu Ren |
| Abstract Scope |
Laser powder bed fusion (LPBF) process is a metal 3D printing technology, where the laser selectively melts powder and fuses it with the underneath substrate based on computer design. Certain defects such as porosity hinders the widespread adoption of LPBF into applications, which have strict quality requirements. One type of porosity defects occurs under some conditions of high laser power and slow scan speed, known as keyhole porosity. We developed an artificial intelligence (AI)-driven approach to detect the pore generation in situ with near-perfect prediction. We used the synchrotron high-speed x-ray imaging as ground truth and acquired simultaneous thermal imaging of the sample surface as training data. We also performed multiphysics simulation to reveal the physical meaning of the features used in the training process. This approach shows a practical way of detecting porosity defects and potential of improving the build parts quality. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |