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.
Turner, John A., et al. "ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure." International Journal of High Performance Computing Applications, vol. 36, no. 1, Jan. 2022. https://doi.org/10.1177/10943420211042558
Turner, John A., Belak, James, Barton, Nathan, et al., "ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure," International Journal of High Performance Computing Applications 36, no. 1 (2022), https://doi.org/10.1177/10943420211042558
@article{osti_1839327,
author = {Turner, John A. and Belak, James and Barton, Nathan and Bement, Matthew and Carlson, Neil and Carson, Robert and DeWitt, Stephen and Fattebert, Jean-Luc and Hodge, Neil and Jibben, Zechariah and others},
title = {ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure},
annote = {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.},
doi = {10.1177/10943420211042558},
url = {https://www.osti.gov/biblio/1839327},
journal = {International Journal of High Performance Computing Applications},
issn = {ISSN 1094-3420},
number = {1},
volume = {36},
place = {United States},
publisher = {SAGE Publications},
year = {2022},
month = {01}}
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
Alternate ID(s):
OSTI ID: 1883992 OSTI ID: 2290298
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
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 378, Issue 2166https://doi.org/10.1098/rsta.2019.0056