| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing Modeling, Simulation and Machine Learning
|
| Presentation Title |
Development of Simulation-based Qualification Data for Laser Powder Bed Fusion Using Modeling and Uncertainty Quantification |
| Author(s) |
Daniel Moser, Kyle Johnson, Michael Stender, Michael Heiden, Theron Rodgers, Nicole Aragon, Aashique Rezwan, Jeffrey Horner, David Saiz, Helen Cleaves |
| On-Site Speaker (Planned) |
Daniel Moser |
| Abstract Scope |
Qualification of parts produced by laser powder bed fusion (LPBF) for failure critical applications remains an outstanding challenge. Qualification often requires rigorous control of processing parameters and extensive and time-consuming inspection routines, significantly cutting into the cost and flexibility benefits of LPBF. Sometimes qualification requirements have prevented the insertion of LPBF parts completely. This work develops simulation tools to provide computational qualification evidence for LPBF. By combining models for microstructure and distortion prediction with uncertainty quantification methods, we make stochastic predictions about LPBF builds based on uncertainties in a ProX 200 LPBF machine. Predictions are compared against multiple experimental builds to assess their ability to capture the range of possible process outputs. Results are intended to reduce the burden on inspection and inform where to invest in process controls.
This work was supported by the LDRD program at SNL, managed and operated by NTESS under DOE NNSA contract DE-NA0003525. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Modeling and Simulation, |