About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing Modeling, Simulation and Artificial Intelligence
|
| Presentation Title |
F-37: Quantifying Process Uncertainty in Laser Powder Bed Fusion: A Modeling Approach for Surface Topography Prediction |
| Author(s) |
Kubra Sekmen, Zekeriya Ender Eger, Pinar Acar, Bart Raeymaekers |
| On-Site Speaker (Planned) |
Zekeriya Ender Eger |
| Abstract Scope |
In Powder Bed Fusion – Laser Beam (PBF-LB), process parameters such as laser power and scan speed are often treated as constants, overlooking their inherent variability in real manufacturing environments. This study presents a modeling framework that incorporates process uncertainty into surface topography properties prediction. Surface measurements of IN718 specimens were used to characterize variability in these properties. In the absence of direct process parameter measurements, multiple distribution types were assumed to represent uncertainty in power and scan speed. These uncertainties were embedded in a non-intrusive polynomial chaos expansion model relating process parameters to surface topography properties. We demonstrated that uncertainty-aware models offer robust prediction with minimal accuracy loss, despite variations in input distributions. Our approach provides an improved understanding of process–surface relationships under uncertainty. It offers a pathway toward predictive control of PBF-LB surface quality and can be extended to different materials, additional process parameters, and broader manufacturing conditions. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, ICME, Modeling and Simulation |