About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Phase Field Modelling of Microstructure Formation in Rapidly Solidified Steel |
Author(s) |
Nikolas Provatas, Salvador Valtierra Rodriguez, Damien Pinto, Michael Greenwood |
On-Site Speaker (Planned) |
Nikolas Provatas |
Abstract Scope |
We present a multi-order phase-field model applied to the study solidification microstructure formation in rapidly solidified AISI309L austenitic stainless steel. We employ data from the CALPHAD literature to capture the thermodynamics of the phases present and use the Schaeffler diagram to express the Chromium and Nickel equivalent compositions. The thermal solidification history used to drive the system is based on a combination of thermocouple and FEM data generated for this study. We examine the spatial statistics of simulated dendritic microstructures and segregation and compare these with corresponding experimental data, finding good comparison. We conclude the talk with a recent machine learning study that uses simulation data to train a neural network algorithm to predict the time-evolution of microstructure and segregation during solidification. We show that this method can complement multi-scale algorithms to further enhance efficiency, or be used in stand-alone mode to predict microstructure evolution. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Solidification, Iron and Steel |