@article {1366,
	title = {Accurate reconstruction of EBSD datasets by a multimodal data approach using an evolutionary algorithm},
	journal = {Materials Characterization},
	volume = {150},
	year = {2019},
	pages = {184{\textendash}198},
	abstract = {<p>
	A new method has been developed for the correction of the distortions and/or enhanced phase differentiation in Electron Backscatter Diffraction (EBSD) data. Using a multi-modal data approach, the method uses segmented images of the phase of interest (laths, precipitates, voids, inclusions) on images gathered by backscattered or secondary electrons of the same area as the EBSD map. The proposed approach then searches for the best transformation to correct their relative distortions and recombines the data in a new EBSD file. Speckles of the features of interest are first segmented in both the EBSD and image data modes. The speckle extracted from the EBSD data is then meshed, and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is implemented to distort the mesh until the speckles superimpose. The quality of the matching is quantified via a score that is linked to the number of overlapping pixels in the speckles. The locations of the points of the distorted mesh are compared to those of the initial positions to create pairs of matching points that are used to calculate the polynomial function that describes the distortion the best. This function is then applied to un-distort the EBSD data, and the phase information is inferred using the data of the segmented speckle. Fast and versatile, this method does not require any human annotation and can be applied to large datasets and wide areas. Besides, this method requires very few assumptions concerning the shape of the distortion function. It can be used for the single compensation of the distortions or combined with the phase differentiation. The accuracy of this method is of the order of the pixel size. Some application examples in multiphase materials with feature sizes down to 1 \&mu;m are presented, including Ti-6Al-4 V Titanium alloy, Rene 65 and additive manufactured Inconel 718 Nickel-base superalloys.
</p>
},
	keywords = {CMA-ES, Distortions, EBSD, Image segmentation, Multi-modal data},
	issn = {10445803},
	doi = {10.1016/j.matchar.2019.01.033},
	url = {https://doi.org/10.1016/j.matchar.2019.01.033},
	author = {Charpagne, Marie Agathe and Strub, Florian and Pollock, Tresa M.}
}
@article {1386,
	title = {Computational homogenization for multiscale forward modeling of resonant ultrasound spectroscopy of heterogeneous materials},
	journal = {Materials Characterization},
	volume = {158},
	year = {2019},
	pages = {109945},
	abstract = {<p>
	We present a computational framework for multiscale forward modeling of ultrasound resonance in heterogeneous materials that accounts for microstructure. The approach includes two steps. The first step is the accurate determination of the elastic properties of heterogeneous materials with finite element simulations on a representative volume element of the microstructure at the mesoscopic length scale. The second step is modeling resonance frequencies of a macroscopic component made of an effective homogeneous medium having the same elastic properties as the actual material with microstructure. The approach is validated in a case study on a Cu\&ndash;W two-phase composite, for which resonance frequencies predicted with the proposed framework are compared against the experimental measurements. The present multiscale modeling approach, involving computational homogenization and leveraging 3D microstructure data, showed better accuracy compared to classical Voigt/Reuss bounds often used for forward modeling of resonant ultrasound spectroscopy.
</p>
},
	keywords = {Computational homogenization, Finite elements, Forward modeling, Non-destructive evaluation, Resonant ultrasound spectroscopy},
	issn = {10445803},
	doi = {10.1016/j.matchar.2019.109945},
	url = {https://doi.org/10.1016/j.matchar.2019.109945},
	author = {Latypov, Marat I. and Charpagne, Marie Agathe and Souther, Mason and Goodlet, Brent R. and Echlin, Mclean P. and Beyerlein, Irene J. and Pollock, Tresa M.}
}
