@article {1676,
	title = {Insight into microstructure-sensitive elastic strain concentrations from integrated computational modeling and digital image correlation},
	journal = {Scripta Materialia},
	volume = {192},
	year = {2021},
	pages = {78{\textendash}82},
	abstract = {<p>
	The microstructural origins of highly localized elastic strain concentrations in polycrystalline microstructures under monotonic loading are studied using grain-scale, in situ digital image correlation and crystal plasticity finite element method. It is shown that the locations of exceptionally high elastic strain concentrations in the microstructure depend on particular crystallographic and morphological orientations of grains and less so on crystalline details of their local neighborhood. Based on these results, we discuss how topological and crystallographic features of annealing twin boundaries can increase the likelihood of slip band initiation throughout the microstructure of polycrystalline Ni-base superalloys.
</p>
},
	keywords = {Elasticity, Micromechanics, Microstructure, Superalloys},
	issn = {13596462},
	doi = {10.1016/j.scriptamat.2020.10.001},
	url = {https://doi.org/10.1016/j.scriptamat.2020.10.001},
	author = {Latypov, Marat I. and Stinville, Jean Charles and Mayeur, Jason R. and Hestroffer, Jonathan M. and Pollock, Tresa M. and Beyerlein, Irene J.}
}
@article {1471,
	title = {Application of chord length distributions and principal component analysis for quantification and representation of diverse polycrystalline microstructures},
	journal = {Materials Characterization},
	volume = {145},
	year = {2018},
	pages = {671{\textendash}685},
	abstract = {<p>
	Quantification of mesoscale microstructures of polycrystalline materials is important for a range of practical tasks of materials design and development. The current protocols of quantifying grain size and morphology often rely on microstructure metrics (e.g., mean grain diameter) that overlook important details of the mesostructure. In this work, we present a quantification framework based on directionally resolved chord length distribution and principal component analysis as a means of extracting additional information from 2-D microstructural maps. Towards this end, we first present in detail a method for calculating chord length distribution based on boundary segments available in modern digital datasets (e.g., from microscopy post-processing) and their low-rank representations by principal component analysis. The utility of the proposed framework for capturing grain size, morphology, and their anisotropy for efficient visualization, representation, and specification of polycrystalline microstructures is then demonstrated in case studies on datasets from synthetic generation, experiments (on Ni-base superalloys), and simulations (on steel during recrystallization).
</p>
},
	keywords = {Chord length distribution, EBSD, Grain size, Microstructure, Principal component analysis},
	issn = {10445803},
	doi = {10.1016/j.matchar.2018.09.020},
	url = {https://doi.org/10.1016/j.matchar.2018.09.020},
	author = {Latypov, Marat I. and K{\"u}hbach, Markus and Beyerlein, Irene J. and Stinville, Jean Charles and Toth, Laszlo S. and Pollock, Tresa M. and Kalidindi, Surya R.}
}
