Coupling in situ experiments and modeling – Opportunities for data fusion,machine learning,and discovery of emergent behavior |
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Affiliation: | 1. Georgia Institute of Technology, School of Materials Science and Engineering, Atlanta, GA 30332, United States;2. Georgia Institute of Technology, Woodruff School of Mechanical Engineering, Atlanta, GA 30332, United States;3. University of Florida, Department of Mechanical & Aerospace Engineering, Gainesville, FL 32611, United States |
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Abstract: | This paper reviews recent studies, that not only includes both experiments and modeling components, but celebrates a close coupling between these techniques, in order to provide insights into the plasticity and failure of polycrystalline metals. Examples are provided of studies across multiple-scales, including, but not limited to, density functional theory combined with atom probe tomography, molecular dynamics combined with in situ transmission electron miscopy, discrete dislocation dynamics combined with nanopillars experiments, crystal plasticity combined with digital image correlation, and crystal plasticity combined with in situ high energy X-ray diffraction. The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation, by providing direct means of comparison, thus removing aspects of epistemic uncertainty in the approach. Further, data fusion between in situ experimental and model-based data, along with data driven approaches, provides a paradigm shift for determining the emergent behavior of deformation and failure, which is the foundation that underpins the mechanical behavior of polycrystalline materials. |
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Keywords: | Microstructure modeling Molecular dynamics In situ transmission electron microscopy Dislocation dynamics Crystal plasticity Digital image correlation In situ high energy X-ray diffraction microscopy Artificial intelligence Data driven approaches Material informatics |
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