@article {1336, title = {Accelerated discovery of oxidation resistant CoNi-base γ/γ{\textquoteright} alloys with high L12 solvus and low density}, journal = {Materials and Design}, volume = {189}, year = {2020}, pages = {1{\textendash}12}, abstract = {

A systematic and accelerated methodology for alloy design is demonstrated by combining combinatorial synthesis, high-throughput oxide scale characterization, computational density functional theory, and CALPHAD database calculations. By applying this strategy to the senary Co-Ni-W-Al-Cr-Ta material system, a novel \γ/\γ\&$\#$39; alloy exhibiting a superior combination of properties has been identified. Properties include a \γ\&$\#$39; solvus near 1200 \°C, continuous \α-Al2O3 scale formation after 1 h in air at 1100 \°C, and comparable density to 2nd-generation commercial Ni-base superalloys. This novel alloy SB-CoNi-10 has a nominal composition of 39.8Co \– 36.5Ni \– 13.2Al \– 1.0W \– 6.0Cr \– 3.5Ta at.\%, and its properties are compared with other recently studied Co-, CoNi-, and Ni-base \γ/\γ\&$\#$39; alloys.

}, keywords = {Alloy design, Cobalt-base superalloys, Combinatorial synthesis, Oxidation, Superalloys}, issn = {18734197}, doi = {10.1016/j.matdes.2019.108445}, author = {Stewart, Colin A. and Murray, Sean P. and Suzuki, Akane and Pollock, Tresa M. and Levi, Carlos G.} } @inbook {1531, title = {Acquisition of 3D Data for Prediction of Monotonic and Cyclic Properties of Superalloys}, booktitle = {Integrated Computational Materials Engineering (ICME): Advancing Computational and Experimental Methods}, year = {2020}, pages = {1{\textendash}18}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

The component design and fatigue life prediction of turbine disk alloys are critically dependent on the thermomechanical properties of the material. The TriBeam microscope provides a pathway to capture large, targeted 3D microstructural data volumes from turbine disk alloys in order to instantiate models that are used to simulate mechanical loading and assess material properties. The TriBeam experiments used to capture the microstructure, the analysis methods, data requirements, and future needs for 3D infrastructure will be discussed.

}, isbn = {978-3-030-40562-5}, doi = {10.1007/978-3-030-40562-5_1}, url = {https://doi.org/10.1007/978-3-030-40562-5_1}, author = {Echlin, Mclean P. and Lenthe, William C. and Stinville, Jean-Charles and Pollock, Tresa M.}, editor = {Ghosh, Somnath and Woodward, Christopher and Przybyla, Craig} } @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 = {

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 \μm are presented, including Ti-6Al-4 V Titanium alloy, Rene 65 and additive manufactured Inconel 718 Nickel-base superalloys.

}, 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 {1431, title = {Advanced detector signal acquisition and electron beam scanning for high resolution SEM imaging}, journal = {Ultramicroscopy}, volume = {195}, year = {2018}, pages = {93{\textendash}100}, abstract = {

The advancement of materials science at the mesoscale requires improvements in both sampling volumes/areas and spatial resolution in order to make statistically significant measurements of microstructures that influence higher-order material properties, such as fatigue and fracture. Therefore, SEM-based techniques have become desirable due to improvements in imaging resolution, large sample handling capability, and flexibility for in-situ instrumentation. By using fast sampling of SEM electron detector signals, intrinsic beam scanning defects have been identified that are related to the response time of the SEM electron beam deflectors and electron detectors. Mitigation of these beam scanning defects using detector sampling approaches and an adaptive model for settling time is shown to produce higher resolution SEM images, at faster image acquisition times, with a means to quantify the different response functions for various beam deflectors and detectors including those for electrons and ions.

}, keywords = {High resolution digital image correlation, Image processing, Imaging defects, Scan generator, Scanning electron microscopy, SEM}, issn = {18792723}, doi = {10.1016/j.ultramic.2018.08.025}, url = {https://doi.org/10.1016/j.ultramic.2018.08.025}, author = {Lenthe, William C. and Stinville, Jean Charles and Echlin, Mclean P. and Chen, Zhe and Daly, Samantha and Pollock, Tresa M.} } @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 = {

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).

}, 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.} }