About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Materials for Qualification and Certification
|
| Presentation Title |
Challenges in Prediction Microstructure Variability in SS316 |
| Author(s) |
Alex J. Plotkowski, Benjamin Stump, John Coleman, Matt Rolchigo, Gerry Knapp |
| On-Site Speaker (Planned) |
Alex J. Plotkowski |
| Abstract Scope |
Stainless steel 316 is a target material for additive manufacturing of nuclear energy components. However, qualification of AM materials is challenged by the variability in properties, especially creep performance, as a function of process parameters, geometry, and between manufacturing systems. Computational tools can help predict variability, but spanning the relevant length and time scales of the physical phenomena are a persistent challenge. The purpose of this work is to develop a computational framework to predict variability in microstructure in SS316 components based on analysis of process data and using physics-based models. Specific challenges in properly capturing the melt pool behavior and grain structure characteristics of SS316L and SS316H will be discussed, revealing a mechanistic understanding of microstructure evolution. Lastly, these capabilities are coupled directly to real-world processing data to enable automated analysis of new builds. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Modeling and Simulation, Solidification |