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Microstructurally-informed stochastic inhomogeneity of material properties and material symmetries in 3D-printed 316 L stainless steel

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Abstract

Stochastic mesoscale inhomogeneity of material properties and material symmetries are investigated in a 3D-printed material. The analysis involves a spatially-dependent characterization of the microstructure in 316 L stainless steel, obtained through electron backscatter diffraction imaging. These data are subsequently fed into a Voigt–Reuss–Hill homogenization approximation to produce maps of elasticity tensor coefficients along the path of experimental probing. Information-theoretic stochastic models corresponding to this stiffness random field are then introduced. The case of orthotropic fields is first defined as a high-fidelity model, the realizations of which are consistent with the elasticity maps. To investigate the role of material symmetries, an isotropic approximation is next introduced through ad-hoc projections (using various metrics). Both stochastic representations are identified using the dataset. In particular, the correlation length along the characterization path is identified using a maximum likelihood estimator. Uncertainty propagation is finally performed on a complex geometry, using a Monte Carlo analysis. It is shown that mechanical predictions in the linear elastic regime are mostly sensitive to material symmetry but weakly depend on the spatial correlation length in the considered propagation scenario.

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Acknowledgements

The work of S.C. was supported by the National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation, under award CMMI-1942928. The work of J.G. was partially supported by the U.S. National Research Laboratory (US NRL) under contract N0017321P1059, as well as by the National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation, under award CMMI-1942928. The work of A.I., J.M., A.B., J.S., and C.S. was supported by the Office of Naval Research through U.S. Naval Research Laboratory’s core funding. This support is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Duke University, Durham, NC, 27708, USA

    Shanshan Chu & Johann Guilleminot

  2. Computational Multiphysics Systems Laboratory, Center of Computational Material Science, Naval Research Laboratory, Washington, DC, 20375, USA

    Athanasios Iliopoulos, John Michopoulos, Andrew Birnbaum & John Steuben

  3. Metallurgical Alloy Design Section, Multifunctional Materials Branch, Naval Research Laboratory, Washington, DC, 20375, USA

    Colin Stewart, Patrick Callahan & David Rowenhorst

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  1. Shanshan Chu
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Chu, S., Iliopoulos, A., Michopoulos, J. et al. Microstructurally-informed stochastic inhomogeneity of material properties and material symmetries in 3D-printed 316 L stainless steel. Comput Mech (2023). https://doi.org/10.1007/s00466-023-02424-6

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