| 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 |
In-Situ Laser Powder Bed Fusion: Real-Time Assessment of Residual Stress through Thermal Gradient Analysis |
| Author(s) |
Xinyi Xiao, Hanbin Xiao |
| On-Site Speaker (Planned) |
Xinyi Xiao |
| Abstract Scope |
Metal additive manufacturing processes have garnered significant attention due to their ability to enhance design flexibility and manufacturability. However, the rapid heating and cooling inherent in these processes often lead to deviations in the as-built properties. Residual stress emerges as a critical factor contributing to these deviations, posing a risk of build failure and adversely affecting the functionality of the fabricated parts. To address these challenges, this study proposes a data-efficient computational machine learning model integrated with process-related physical phenomena. By establishing a quantitative link between observed in-situ phenomena and residual stress, the framework facilitates a deeper understanding of the metal AM process. The developed model not only enhances the ease-of-use but also converts real-time monitoring data into prognostic signals, providing insights into the metal AM process's quality. This approach is instrumental in predicting and controlling the as-built residual stress, ultimately improving the overall quality of fabricated metal AM parts. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |