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Multi-scale Microstructure and Property-Based Statistically Equivalent RVEs for Modeling Nickel-Based Superalloys

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Integrated Computational Materials Engineering (ICME)

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Abstract

This chapter discusses fundamental aspects of the development of statistically equivalent virtual microstructures (SEVMs) and microstructure and property-based statistically equivalent representative volume elements (M-SERVE and P-SERVE) of the Ni-based superalloy at multiple scales. The two specific scales considered for this development are the subgrain scale of intragranular γ − γ′ microstructures and the polycrystalline scale of grain ensembles with annealing twins. A comprehensive suite of computational methods that can translate microstructural data in experimental methods to optimally defined representative volumes for effective micromechanical modeling is the objective of this study. The framework involves a sequence of tasks, viz., serial sectioning, image processing, feature extraction, and statistical characterization, followed by micromechanical analysis and convergence tests for statistical functions. A principal motivation behind this paper is to translate high-fidelity microstructural image data into statistics of parametric descriptors in constitutive laws governing material performance.

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Acknowledgements

This work has been supported through a grant No. FA9550-12-1-0445 to the Center of Excellence on Integrated Materials Modeling (CEIMM) at Johns Hopkins University awarded by the AFOSR/RSL Computational Mathematics Program (Manager Dr. A. Sayir) and AFRL/RX (Monitors Drs. C. Woodward and C. Przybyla). These sponsorships are gratefully acknowledged. Computing support by the Homewood High Performance Compute Cluster (HHPC) and Maryland Advanced Research Computing Center (MARCC) is gratefully acknowledged.

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Authors and Affiliations

  1. Departments of Civil, Mechanical Engineering and Materials Science & Engineering, Johns Hopkins University, Baltimore, MD, USA

    Somnath Ghosh

  2. Departments of Mechanical and Civil Engineering, Johns Hopkins University, Baltimore, MD, USA

    George Weber, Maxwell Pinz & Akbar Bagri

  3. The Materials Department, University of California Santa Barbara, Santa Barbara, CA, USA

    Tresa M. Pollock, Will Lenthe & Jean-Charles Stinville

  4. Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Dayton, OH, USA

    Michael D. Uchic

  5. Air Force Research Laboratory/RX, Wright-Patterson Air Force Base, Dayton, OH, USA

    Christopher Woodward

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  1. Somnath Ghosh
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  2. George Weber
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  3. Maxwell Pinz
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  4. Akbar Bagri
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  7. Jean-Charles Stinville
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  8. Michael D. Uchic
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  9. Christopher Woodward
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Correspondence to Somnath Ghosh .

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Editors and Affiliations

  1. Departments of Civil, Mechanical Engineering and Materials Science & Engineering, Johns Hopkins University, Baltimore, MD, USA

    Somnath Ghosh

  2. Air Force Research Laboratory/RX, Wright-Patterson Air Force Base, Dayton, OH, USA

    Christopher Woodward

  3. Air Force Research Laboratory/RX, Wright-Patterson Air Force Base, Dayton, OH, USA

    Craig Przybyla

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Ghosh, S. et al. (2020). Multi-scale Microstructure and Property-Based Statistically Equivalent RVEs for Modeling Nickel-Based Superalloys. In: Ghosh, S., Woodward, C., Przybyla, C. (eds) Integrated Computational Materials Engineering (ICME). Springer, Cham. https://doi.org/10.1007/978-3-030-40562-5_3

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