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Machine learning-enabled self-consistent parametrically-upscaled crystal plasticity model for Ni-based superalloys by George Weber; Maxwell Pinz; Somnath Ghosh is a Engineering article available to read on EtoBox.

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This paper introduces a concurrent multiscale modeling framework for developing parametrically-upscaled crystal plasticity models (PUCPM) for crystalline metals that are characterized by multiple phases in their intragranular microstructure. Specifically, Ni-based superalloys with distributions of γ ′ precipitates in the γ matrix in their sub-grain microstructure are modeled in this study. The interaction of the γ − γ ′ phases with nano-scale dislocation mechanisms determine the elasto-plastic behavior of the material across multiple scales. The PUCPM is designed to explicitly account for the morphological and configurational statistics of these γ − γ ′ intragranular microstructures in its crystal plasticity constitutive coefficients. This approach provides a thermodynamically-consistent foundation to enable microstructure-aware material simulation at the higher scale. Establishing this multiscale characteristic requires an automated toolchain of computational methods to generate and embed heterogeneous, statistically-equivalent representative volume elements (SERVEs) into the concurrent multiscale simulation domains for self-consistent homogenization. The self-consistency conditio

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Author

George Weber; Maxwell Pinz; Somnath Ghosh

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Engineering (Physical Sciences)