About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Additive Manufacturing for Energy Applications V
|
Presentation Title |
Quantification of Uncertainties in Metal Additive Manufacturing Processes in Support of Qualification |
Author(s) |
Daniel Moser, Helen Cleaves, Michael Heiden, Scott Jensen, Kyle Johnson, Mario Martinez, Theron Rodgers, David Saiz, Michael Stender |
On-Site Speaker (Planned) |
Daniel Moser |
Abstract Scope |
Qualification of metal additive processes is difficult partly because variabilities in process outcomes are observed even when process inputs are tightly controlled. These repeatability challenges can make qualification ill-defined, particularly for failure-critical applications. This work attempts to address this using computational modeling and uncertainty quantification (UQ) techniques to predict the effect of machine variabilities on part properties for laser powder bed fusion (LPBF). An uncertainty inventory of an LBPF machine is compiled. Probability distributions for the uncertainties are measured or estimated and UQ techniques used to propagate these distributions through physical models to predict distributions for output quantities of interest including melt pool shapes, as-built part geometries, residual stresses, and microstructural features. Large volumes of test artifacts are then produced to experimentally quantify variability and compare to computationally predicted distributions.
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, Computational Materials Science & Engineering, Other |