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• W4317633797 abstract "This paper develops a Bayesian inference-based probabilistic crack nucleation model for a Ni-based superalloy under fatigue loading. A data-driven, machine learning approach is developed to identify the underlying mechanisms driving crack nucleation. For this model, experimental data is taken from a set of fatigue-loaded microstructures near crack nucleation sites using scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) images for correlating the grain morphology and crystallography to the spatial location of crack nucleation sites. A concurrent multiscale model that embeds experimentally acquired polycrystalline microstructural RVEs in a homogenized material, is developed for fatigue simulations. The RVE domain is modeled by a crystal plasticity FEM, while a self-consistent homogenized anisotropic plasticity model is used for the exterior domain. The simulations create a database of state variables to be used in the model development. A Bayesian classification method is introduced to optimally select the most informative state variable predictors of crack nucleation and constructs a near-Pareto frontier of models with varying complexity. From this set of state variables, a simple scalar crack nucleation indicator is formulated, which encompasses relevant components derived from the main discriminators. The resulting model predicts the probability of nucleating a crack at a microstructural location, given the mechanical state of the material." @default.
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• W4317633797 date "2023-01-19" @default.
• W4317633797 modified "2023-09-29" @default.
• W4317633797 title "Data-Driven Bayesian Model for Predicting Fatigue Crack Nucleation in Polycrystalline Ni-based Superalloys" @default.
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• W4317633797 doi "https://doi.org/10.2514/6.2023-2036" @default.
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