About this Abstract |
| Meeting |
TMS Specialty Congress 2026
|
| Symposium
|
World Congress on Reproducibility, Qualification, and Standards Development of Additive Manufacturing and Beyond (RQSD 2026)
|
| Presentation Title |
Probabilistic Fatigue Life Prediction for Selective Laser Melting Additive Manufacturing |
| Author(s) |
Yulin Guo, Boris Kramer, Veera Sundararaghavan |
| On-Site Speaker (Planned) |
Yulin Guo |
| Abstract Scope |
Fatigue life prediction with uncertainty bounds is essential for qualification of critical laser powder bed fusion components. We present a framework that propagates uncertainties from in-situ sensor measurements to fatigue life distributions. We obtain microstructure and defect characterizations from optical and acoustic sensor data and use them in multi-fidelity simulations to compute fatigue life-related quantities, namely stress intensity factor, fatigue indicator parameter, and microstructural crack path energy. We first develop closed-form probability density functions for fatigue life based on assumed normal and lognormal distributions of these fatigue-controlling quantities. As computational budget permits, we perform more simulations and refine the fatigue life distribution by fitting the simulation results to more appropriate distributions. We validate the framework's accuracy using Monte Carlo sampling benchmarks. This approach provides uncertainty bounds on fatigue life rather than conservative deterministic predictions and supports informed decision making for additive manufacturing. |
| Proceedings Inclusion? |
Undecided |