About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
| Presentation Title |
Global Sensitivity Process Diagrams to Visualize the Impact of Composition Variability on Laser-based Powder Bed Fusion of Nickel Alloy 718 |
| Author(s) |
Li Ma, Pranav Karve, Hasan Jame, Sankaran Mahadevan, Mohadeseh S. Taheri-Mousavi, Steven Storck, Morgan Trexler, Somnath Ghosh, Anthony Rollett, Brendan Croom |
| On-Site Speaker (Planned) |
Li Ma |
| Abstract Scope |
A computational modeling pipeline (ThermoCalc and computational fluid dynamics, or CFD) is developed to quantify the effect of feedstock composition variability on melt pool geometry, thermal gradient, and cooling rate (quantities of interest, or QoIs) for Nickel alloy 718 manufactured by powder bed fusion-laser beam (PBF-LB). Melt pool dimensions from single track PBF-LB experiments using feedstock from multiple vendors are used to validate the modeling pipeline. It is shown that variability in feedstock leads to variability in thermophysical properties, which propagates to the QoIs. PBF-LB process simulations are then performed to obtain data used for training surrogate models that predict the QoIs for different chemical compositions, for fixed (given) process parameters. Global Sensitivity Analysis (GSA) is conducted to evaluate the impact of uncertainty in concentration of each element on OoIs. A global sensitivity process diagram for Nickel alloy 718 is developed to visualize GSA results for different process parameters. |