Lawrence Livermore National Laboratory



This 3-dimensional simulation illustrates the coring effect on microstructure evolution that takes place during solidification in a gold-nickel alloy. Visible in this micrometer-scale simulation - leveraging high-performance computers available at Lawrence Livermore - is how a gradient of composition develops within the solid grains, which has a direct impact on materials properties such as strength and conductivity, and subsequently on materials performance of 3D-printed parts.

Modeling the properties of 3D-printed metals at the microstructure scale

Modeling how melted powder grains cool and change phases at the microstructure scale is a critical element in linking additive manufacturing processes with the properties of additively manufactured metal parts.

Paired with microstructure modeling as part of the internally funded strategic initiative to accelerate the certificaiton of additively manufactured metals, this effort also includes thorough characterization of sample material microstructures to validate the models.

Using thermal history from powder and effective medium models, the microstructure model simulates melting, solidification, and solid-state phase transformations. The time scale is fractions of a second and the length scales are hundreds of micrometers.

The development of this model builds on the Laboratory's long history in characterizing materials and processes. The goal is to predict how an additive manufacturing process impacts microstructural features of materials in terms of precipitates, grain boundaries and dislocation network.

To do this, Lawrence Livermore is using a mesoscale model and characterization of sample microstructures. We are looking in detail at features like grain size, distributions and defects caused by the manufacturing process, all of which impact the properties of the part produced.

This specific activity provides microstructure input data needed for the effective medium and performance modeling. Sensitivity analyses (or uncertainty quantification) are being applied to identify the critical parameters of this mesoscale modeling.

We make use of the coarse-grained, phase-field mesoscale AMPE code (Dorr et al.: 2010) that requires, in the framework of the computer coupling of phase diagrams and thermochemistry (CALPHAD) methodology (Turchi et al.: 2007), the following input as functions of alloy composition and temperature:

  • Thermodynamic driving force
  • Species mobilities (diffusion coefficients)
  • Temperature-time-transformation diagrams.

Patrice Turchi

  • turchi1@llnl.gov

    Group Leader, Advanced Metallurgical Science & Engineering

    Patrice Turchi

  • turchi1@llnl.gov

    Group Leader, Advanced Metallurgical Science & Engineering