Applications of CALPHAD Based Tools to Additive Manufacturing Models

Wednesday, May 8, 2019: 2:30 PM
Cascade 1 (Nugget Casino Resort)
Mr. Paul Mason , Thermo-Calc Software Inc., McMurray, PA
Dr. Adam T. Hope , Thermo-Calc Software, McMurray, PA
Dr. Kaisheng Wu , Thermo-Calc Software Inc., McMurray, PA
The publication by the National Academies in 2008 on Integrated Computational Materials Engineering (ICME) highlighted the need for better multiscale materials modeling to capture the process-structures-properties-performance of a material. This is especially true in the case of additive manufacturing where it is almost impossible to model the process without considering solidification, thermal cycling and microstructural and compositional changes in an integrated fashion.

Computational thermodynamics, and specifically CALPHAD, allows for the prediction of the thermodynamic properties and phase stability of an alloy, under stable and metastable conditions. Additionally, the CALPHAD approach can also be extended to model atomic mobilities and diffusivities in a similar way. By combining thermodynamic and mobility data, kinetic reactions during solidification and subsequent heat treatment processes can then be simulated. By integrating computational thermodynamics and CALPHAD based tools into an ICME framework, it is possible to optimize alloy compositions and solution heat treatment temperature ranges, as well as predict optimal solidification processes without performing many time-consuming and costly experiments.

This presentation will highlight three examples where CALPHAD tools have been applied to understanding and solving materials challenges relevant to additive manufacturing. Specifically:

  1. Improving Finite Element modelling with CALPHAD data. Using the CALPHAD approach, properties such as density, specific heat, and enthalpy can be calculated, as a function of composition and temperature. These can then be used in lieu of handbook values to provide more accurate data for FEM/FEA simulations.
  2. Predicting phase balance during multiple heating/cooling cycles which are symptomatic of the additive process, which makes both controlling and predicting the resultant microstructure more difficult.

  3. Predicting optimal post-build heat treatments, particularly where typical stress relief heat treatment temperatures are not suitable due to local inhomogeneities in alloy composition arising from rapid solidification.