Sparse thermal data for cellular automata modeling of grain structure in additive manufacturing

Rolchigo, Matthew; Stump, Benjamin; Belak, James; Plotkowski, Alex

doi:10.1088/1361-651x/ab9734

Title: Sparse thermal data for cellular automata modeling of grain structure in additive manufacturing

Journal Article · Fri Jun 19 00:00:00 EDT 2020 · Modelling and Simulation in Materials Science and Engineering

DOI:https://doi.org/10.1088/1361-651x/ab9734· OSTI ID:1642368

^[1];

^[2]; Belak, James ^[1]; Plotkowski, Alex ^[2]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Grain growth in the wake of the melt pool formed during alloy-based additive manufacturing (AM) is complex and multifaceted, depending on parameters governing heat transport, fluid flow, and solidification itself. Cellular automata (CA) models have proven effective in providing computationally efficient and physically sound predictions of grain structure for several AM problems, but their efficiency is tied to the performance of heat transport models. CA models use only a small portion of the problem's temperature data (near the moving melt pool boundary), and much of the CA calculations do not affect the final result due to re-melting of material. Coupling of and communication between heat transport and solidification models, and eliminating operations irrelevant towards final grain structure prediction, will be necessary for using these methods for efficient simulation of large parts. We introduce a procedure of decoupling the CA from temperature field simulation, using files of relevant temperature data written by the heat transport model. This approach is validated against the standard coupling approach using data obtained through the computational fluid dynamics software OpenFOAM. Negligible differences are seen in grain size, volume, and texture distributions for multilayer simulation of test problems, while the quantity of temperature data for these test problems was reduced by four orders of magnitude (from 100s of GB to 10s of MB) and the code performance sped up by a factor of around 50. Variability in microstructure as a function of cell size, substrate, time step, and nucleation parameters is studied, and it is found that cell sizes less than or equal to 1.67 μm and sufficiently small time steps yield statistically equivalent microstructures. Finally, a potential use case for this CA approach—the layer-wise convergence in grain structure starting from extremes in initial grain size—is examined. This approach's ability to simulate expected trends in nucleation and epitaxial grain growth for large regions of microstructure, simulated independently of heat transport models themselves, should prove useful for investigation of various microstructure uncertainties and prediction of part-scale experimental results.

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Research Organization:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); Exascale Computing Project; USDOE Office of Science (SC)

Grant/Contract Number:: AC52-07NA27344; AC05-00OR22725; 17-SC-20-SC

OSTI ID:: 1642368

Alternate ID(s):: OSTI ID: 1649339

Report Number(s):: LLNL-JRNL-804281; 1006368

Journal Information:: Modelling and Simulation in Materials Science and Engineering, Vol. 28, Issue 6; ISSN 0965-0393

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 13 works

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