ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Turner, John A.; Belak, James; Barton, Nathan; Bement, Matthew; Carlson, Neil; Carson, Robert; DeWitt, Stephen; Fattebert, Jean-Luc; Hodge, Neil; Jibben, Zechariah; King, Wayne; Levine, Lyle; Newman, Christopher; Plotkowski, Alex; Radhakrishnan, Balasubramaniam; Reeve, Samuel Temple; Rolchigo, Matthew; Sabau, Adrian; Slattery, Stuart; Stump, Benjamin

doi:10.1177/10943420211042558

Title: ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Journal Article · 09 January 2022 · International Journal of High Performance Computing Applications

DOI: https://doi.org/10.1177/10943420211042558 · OSTI ID:1839327

^[1]; Belak, James ^[2]; Barton, Nathan ^[2]; Bement, Matthew ^[1]; Carlson, Neil ^[3];

^[2]; DeWitt, Stephen ^[1]; Fattebert, Jean-Luc ^[1]; Hodge, Neil ^[2];

^[3]; King, Wayne ^[4]; Levine, Lyle ^[5]; Newman, Christopher ^[3]; Plotkowski, Alex ^[1]; Radhakrishnan, Balasubramaniam ^[1]; Reeve, Samuel Temple ^[1]; Rolchigo, Matthew ^[2]; Sabau, Adrian ^[1]; Slattery, Stuart ^[1]; Stump, Benjamin ^[1]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN, USA
Lawrence Livermore National Laboratory (LLNL), Livermore, CA, USA
Los Alamos National Laboratory (LANL), Los Alamos, NM, USA
The Barnes Global Advisors, Stuart, FL, USA
National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

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

Grant/Contract Number:: 89233218CNA000001; AC05-00OR22725; AC52-07NA27344

OSTI ID:: 1839327

Report Number(s):: LA-UR--21-27894

Journal Information:: International Journal of High Performance Computing Applications, Journal Name: International Journal of High Performance Computing Applications Journal Issue: 1 Vol. 36; ISSN 1094-3420

Publisher:: SAGE PublicationsCopyright Statement

Country of Publication:: United States

Language:: English

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