A mesoscopic digital twin that bridges length and time scales for control of additively manufactured metal microstructures

Tae Wook Heo, Saad A. Khairallah, Rongpei Shi, Joel Berry, Aurelien Perron, Nicholas P. Calta, Aiden A. Martin, Nathan R. Barton, John Roehling, Tien Roehling, Jean Luc Fattebert, Andy Anderson, Albert L. Nichols, Steven Wopschall, Wayne E. King, Joseph T. McKeown, Manyalibo J. Matthews

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

We present our recent development of an integrated mesoscale digital twin (DT) framework for relating processing conditions, microstructures, and mechanical responses of additively manufactured (AM) metals. In particular, focusing on the laser powder bed fusion technique, we describe how individual modeling and simulation capabilities are coupled to investigate and control AM microstructural features at multiple length and time scales. We review our prior case studies that demonstrate the integrated modeling schemes, in which high-fidelity melt pool dynamics simulations provide accurate local thermal profiles and histories to subsequent AM microstructure simulations. We also report our new mechanical response modeling results for predicted AM microstructures. In addition, we illustrate how our DT framework has been validated through modeling–experiment integration, as well as how it has been practically utilized to guide and analyze AM experiments. Finally, we share our perspectives on future directions of further development of the DT framework for more efficient, accurate predictions and wider ranges of applications.

Original languageEnglish
Article number034012 034012
JournalJPhys Materials
Volume4
Issue number3
DOIs
StatePublished - Jul 2021

Funding

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344. This work was funded by the Laboratory Directed Research and Development program at LLNL under the project tracking code 18-SI-003. A P, J-L F, and W K acknowledge the support from the High-Performance Computing for Manufacturing program under tracking code HPC4Mfg-42141.

FundersFunder number
High-Performance Computing for ManufacturingHPC4Mfg-42141
U.S. Department of Energy
Lawrence Livermore National LaboratoryDE-AC52-07NA27344
Laboratory Directed Research and Development18-SI-003

    Keywords

    • Additive manufacturing
    • Digital twin
    • Metals
    • Microstructures
    • Processing
    • Properties

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