Identfication and validation of a stochastic model for mesoscale material description of metallic polycrystals
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This paper is concerned with the identification and validation of a stochastic mesoscale material description for metallic polycrystals. For this purpose we make use of a bounded random matrix model characterizing the mesoscale elasticity tensor of heterogeneous ma- terial. The bounded random matrix exhibits uctuations that are connected to fine scale features through a calibration process performed using a micromechanical framework. The experimental calibration and validation of the model requires testing of a statistically mean- ingful number of samples possessing statistically similar microstructures. To that aim we first employ a statistical model for generating 2D polycrystals that are consistent with the available microstructural measurements on the geometry and crystallographic orienta- tions. The calibration of the mesoscale probabilistic model using realizations of digitally generated polycrystalline microstructures is briey discussed. We then present validation of the probabilistic model from simulated data resulting from subscale simulations. It is found that the probabilistic model for bounded mesoscale elasticity matrix is adequate to predict the response quantity of interest. The scatters in the model predictions are found to be consistent with the fine scale response. The proposed probabilistic model combined with the finite element analysis can be used as a predictive tool in the system level in the context of structural health monitoring and damage prognosis. 2012 AIAA.
