| About this Abstract |
| Meeting |
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Symposium
|
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Presentation Title |
Predicting Material Behavior with Improved Solidification Models for the AM Process Window |
| Author(s) |
Adam T. Hope, Kaisheng Wu, Jan Julin, Johan Jeppsson, Paul Mason |
| On-Site Speaker (Planned) |
Adam T. Hope |
| Abstract Scope |
Predicting localized material properties in additive manufacturing relies on linking chemistry and processing conditions to microstructure in ICME frameworks. In earlier work, using CALPHAD thermodynamics and the Scheil Gulliver equation to generate composition and temperature dependent data for latent heat and heat capacity has been shown to improve the accuracy of finite element simulations to predict the size, shape, and temperature of the laser melt pool. However, rapid solidification, typical of AM processes, can lead to solute trapping, where solute may be incorporated into the solid phase at a concentration significantly different from that predicted by equilibrium thermodynamics. To account for this effect, solute trapping models by Aziz and Kaplan have been incorporated into the CALPHAD/Scheil methodology. A case study is presented to show how this can lead to better microstructure prediction in Alloy 718 and the effect of solute trapping on thermophysical properties to improve finite element modeling. |
| Proceedings Inclusion? |
Undecided |