Abstract
Process-structure-property relationships are the hallmark of materials science. Many integrated computational materials engineering (ICME) models have been developed at multiple length-scales and time-scales, where uncertainty quantification (UQ) plays an important role in quality assurance. In this paper, we applied our previous work [39] to learn a distribution of microstructure features that are consistent in the sense that the forward propagation of this distribution through a crystal plasticity finite element model (CPFEM) matches a target distribution on materials properties, which is given beforehand. To demonstrate the approach, DAMASK and DREAM.3D are employed to construct Hall-Petch relationship for a twinning-induced plasticity (TWIP) steel, where the average grain size distribution is inferred, given a distribution of offset yield strength.
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