About this Abstract |
| Meeting |
2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
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| Symposium
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2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
|
| Presentation Title |
Quantifying Process Uncertainty in Laser Powder Bed Fusion: A Modeling Approach for Surface Topography Prediction |
| Author(s) |
Kubra Sekmen, Pinar Acar, Bart Raeymaekers |
| On-Site Speaker (Planned) |
Kubra Sekmen |
| Abstract Scope |
In Powder Bed Fusion – Laser Beam (PBF-LB), process parameters such as laser power and scan speed are treated as constants, overlooking their inherent variability in real manufacturing environments. This study presents a modeling framework that incorporates process uncertainty into surface topography parameters 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 parameters. 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? |
Undecided |