A Gaussian process autoregressive model capturing microstructure evolution paths in a Ni–Mo–Nb alloy

Journal Article · 20 February 2024 · Journal of Materials Science

DOI: https://doi.org/10.1007/s10853-024-09345-6 · OSTI ID:2429859

Marshall, Andrew ^[1]; Generale, Adam ^[1]; Kalidindi, Surya R.  ^[1]; Radhakrishnan, Bala  ^[2]; Belak, Jim ^[3]

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1. Georgia Institute of Technology, Atlanta, GA (United States)
2. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
3. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Additive manufacturing is increasingly being employed to produce components of complex geometries in structural alloys because of the expected energy savings associated with the near-net-shape capability and the ability to build in novel internal features that are not possible with many conventional manufacturing approaches. However, because of the extreme thermal conditions encountered, the non-equilibrium microstructures produced during powder bed-based additive manufacturing processes must be subjected to custom post-heat treatment processes to recover the target mechanical properties. Phase-field models and simulation techniques have matured to a state where the microstructure evolution paths, and the morphologies of the resulting precipitate phases can be predicted reasonably accurately, considering alloy-specific thermodynamic and kinetic aspects of the nucleation and growth processes. However, phase-field simulations are computationally intensive, which precludes the ability to apply the simulations directly to the length scale of the entire component. Therefore, it is highly desirable to develop low-computational-cost surrogate models that effectively capture the physics at the microstructural length scale, while facilitating the design of optimized processing conditions resulting in location-specific targeted microstructures at the component scale. The work presented here demonstrates the application of the materials knowledge system framework to develop a surrogate model that effectively captures the microstructural path during annealing of a Ni–Mo–Nb alloy containing different Mo and Nb compositions known to segregate during solidification under additive manufacturing conditions. Specifically, the surrogate model built in this work is based on a Gaussian process autoregressive model informed by statistical representation of simulated microstructures using two-point correlations and dimensionality reduction through principal component analysis. In conclusion, this surrogate model is shown to capture the bifurcation of the microstructural path during precipitation, which yields a microstructure dominated by the $$\gamma^{\prime\prime}$$ phase at high Nb concentrations and the $$\delta$$ phase at low Nb concentrations.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 2429859
• Journal Information: Journal of Materials Science, Journal Name: Journal of Materials Science Journal Issue: 12 Vol. 59; ISSN 0022-2461
• Publisher: SpringerCopyright Statement
• Country of Publication: United States
• Language: English