Improved thermodynamic models for predicting thermophysical properties during very rapid solidification

Mason, Paul

During additive manufacturing of metals, extreme cooling rates and thermal cycling can lead to material properties that differ from traditional cast or wrought materials. Material properties and behavior are strongly dependent on the chemical composition of the material and variations in processing conditions. Handbook data typically exists only for the most common materials and does not capture differences arising from composition variations or processing conditions, especially the extreme cooling rates and thermal cycling experienced during most additive manufacturing processes.

To fill the data gaps, CALPHAD-based tools can be used to simulate these highly non-equilibrium processes and aid in the design of new alloys suited to additive manufacturing.

Specifically, CALPHAD tools can be used to calculate the following as a function of material chemistry and temperature:

Thermophysical properties such as heat capacity, enthalpy, and density under non-equilibrium conditions arising from rapid cooling for input into FEA codes
Microsegregation during solidification
Precipitate evolution and growth during heating and cooling cycles
Selecting temperatures for optimal post-build heat treatments

Coupled with FEA codes, these can be used to predict localized material properties by linking chemistry and processing conditions to microstructure. This talk highlights a few such Integrated Computational Materials Engineering (ICME) approaches. In one, the Scheil Gulliver equation was used to generate composition and temperature dependent data for latent heat and heat capacity, leading to improvements in the accuracy of finite element simulations to predict the size, shape, and temperature of the laser melt pool in 316L. Additionally, solute trapping models by Aziz and Kaplan are incorporated into the Scheil methodology, which takes into account the effect of very rapid solidification. 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.