Robert Carson
ABSTRACT
Metal additive manufacturing (AM) is a disruptive manufacturing technology that opens the design space for parts outside those possible from traditional manufacturing methods. In order to accelerate industry and R&D needs to certify AM parts, the Exascale Additive Manufacturing project (ExaAM) has developed a suite of exascale-ready computational tools to model the process-to-structure-to-properties (PSP) relationship for additively manufactured metal components. One such tool is an uncertainty quantification (UQ) pipeline to quantify the effect that uncertainty in processing conditions has on local mechanical responses. We present an overview of this pipeline and its required simulation and workflow codes. Using the Oak Ridge National Laboratory’s (ORNL) exascale computer, Frontier, we utilize this pipeline to cross multiple length and time scales to predict the local mechanical response of a location within a complex AM bridge part, AMB2018-01 produced by the National Institute of Standards and Technology (NIST) as part of their 2018 AM-Bench test series. Our results are then compared to experimental mechanical tests of parts from the NIST build to quantify the error in the ExaAM UQ workflow.
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- Lyle Levine, Brandon Lane, Jarred Heigel, Kalman Migler, Mark Stoudt, Thien Phan, Richard Ricker, Maria Strantza, Michael Hill, Fan Zhang, Jonathan Seppala, Edward Garboczi, Erich Bain, Daniel Cole, Andrew Allen, Jason Fox, and Carelyn Campbell. 2020. Outcomes and Conclusions from the 2018 AM-Bench Measurements, Challenge Problems, Modeling Submissions, and Conference. Integrating Materials and Manufacturing Innovation 9, 1 (Feb 2020), 1–15. https://doi.org/10.1007/s40192-019-00164-1Google ScholarCross Ref
- Matt Rolchigo, Samuel Temple Reeve, Benjamin Stump, Gerald L. Knapp, John Coleman, Alex Plotkowski, and James Belak. 2022. ExaCA: A performance portable exascale cellular automata application for alloy solidification modeling. Computational Materials Science 214 (nov 2022), 111692. https://doi.org/10.1016/j.commatsci.2022.111692Google ScholarCross Ref
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- John A Turner, James Belak, Nathan Barton, Matthew Bement, Neil Carlson, Robert Carson, Stephen DeWitt, Jean-Luc Fattebert, Neil Hodge, Zechariah Jibben, Wayne King, Lyle Levine, Christopher Newman, Alex Plotkowski, Balasubramaniam Radhakrishnan, Samuel Temple Reeve, Matthew Rolchigo, Adrian Sabau, Stuart Slattery, and Benjamin Stump. 2022. ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure. The International Journal of High Performance Computing Applications 36, 1 (Jan 2022), 13–39. https://doi.org/10.1177/10943420211042558Google ScholarDigital Library
Index Terms
- Uncertainty Quantification of Metal Additive Manufacturing Processing Conditions Through the use of Exascale Computing
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