Design, fabrication and characterization of FeAl-based metallic-intermetallic laminate (MIL) composites

Wang, Haoren; Harrington, Tyler; Zhu, Chaoyi; Vecchio, Kenneth S.

doi:10.1016/j.actamat.2019.06.039

Cited by 37 publications

(10 citation statements)

References 43 publications

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“…Nanolayered, two-phase FeAl/FeAl 2 composites have been studied at elevated temperatures, but in this case, the FeAl 2 phase creeps (31). To form a nanolayered Fe-Al system with an even higher density of biphase interfaces, stacks of multiple Fe/FeAl alloy foils were sintered (32). While high, room-temperature strength was achieved, the Fe/FeAl interfaces between the foils eventually delaminated under further deformation (32).…”

Section: Introductionmentioning

confidence: 99%

Achieving room-temperature brittle-to-ductile transition in ultrafine layered Fe-Al alloys

Beyerlein

et al. 2020

Sci. Adv.

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Fe-Al compounds are of interest due to their combination of light weight, high strength, and wear and corrosion resistance, but new forms that are also ductile are needed for their widespread use. The challenge in developing Fe-Al compositions that are both lightweight and ductile lies in the intrinsic tradeoff between Al concentration and brittle-to-ductile transition temperature. Here, we show that a room-temperature, ductile-like response can be attained in a FeAl/FeAl2 layered composite. Transmission electron microscopy, nanomechanical testing, and ab initio calculations find a critical layer thickness on the order of 1 μm, below which the FeAl2 layer homogeneously codeforms with the FeAl layer. The FeAl2 layer undergoes a fundamental change from multimodal, contained slip to unimodal slip that is aligned and fully transmitting across the FeAl/FeAl2 interface. Lightweight Fe-Al alloys with room-temperature, ductile-like responses can inspire new applications in reactor systems and other structural applications for extreme environments.

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Section: Introductionmentioning

confidence: 99%

Achieving room-temperature brittle-to-ductile transition in ultrafine layered Fe-Al alloys

Beyerlein

et al. 2020

Sci. Adv.

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“…By comparison, a CNN trained on a small subset of patterns belonging to each phase produces the phase map seen in Figure 4f. While the phases are not as perfectly linear as in Figure 4e, it is likely that the merging of phases at their borders is real to some degree according to the analysis performed by Wang et al (2019) and their observation that crystal orientation influences the diffusion rates. Otherwise, the phase map is in good agreement with the expected results, and the CNN-based EBSD method is adept at separating diffraction patterns based on space groups and small changes in chemistry within the same space group.…”

Section: Resultsmentioning

confidence: 95%

“…Six different multiphase materials were selected for demonstrating the proposed phase-mapping methodology: (1) a rutilated quartz sample, (2) a sample of the Campo del Cielo meteorite, from the Chaco Province, Argentina, (3) an arc-melted ingot of Ni 80.8 B 13.6 Si 5.4 Fe 0.2 (at%) blended with 40 wt% eutectic tungsten carbide (a metal matrix composite) (Maroli & Liu, 2017), (4) an Fe–Al metallic–intermetallic laminate (MIL) composite (Wang et al, 2019), (5) a thermally cycled MCrAlY-based thermal barrier coating (Evans et al, 2001; Mercer et al, 2006; Pollock et al, 2012), and (6) a sample of 430 stainless steel. Diffraction patterns from 28 other materials, detailed in Kaufmann et al (2020), were utilized for training the CNN-based model for Bravais lattices demonstrated herein.…”

Section: Methodsmentioning

confidence: 99%

“…The Fe-Al MIL composite is presented herein as four distinct phases. This material was fabricated via diffusion controlled growth from a "multiple-thin foil" configuration and a two-stage reaction process described elsewhere (Wang et al, 2019). This fabrication strategy yields layers of pure iron, an Al-enriched α-Fe layer, an Fe-enriched FeAl B2 layer, and a near-equiatomic (∼48 at.% Fe) FeAl B2 layer.…”

Section: Neural Network Architecture and Phase-mapping Proceduresmentioning

confidence: 99%

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Phase Mapping in EBSD Using Convolutional Neural Networks

et al. 2020

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The emergence of commercial electron backscatter diffraction (EBSD) equipment ushered in an era of information rich maps produced by determining the orientation of user-selected crystal structures. Since then, a technological revolution has occurred in the quality, rate detection, and analysis of these diffractions patterns. The next revolution in EBSD is the ability to directly utilize the information rich diffraction patterns in a high-throughput manner. Aided by machine learning techniques, this new methodology is, as demonstrated herein, capable of accurately separating phases in a material by crystal symmetry, chemistry, and even lattice parameters with fewer human decisions. This work is the first demonstration of such capabilities and addresses many of the major challenges faced in modern EBSD. Diffraction patterns are collected from a variety of samples, and a convolutional neural network, a type of machine learning algorithm, is trained to autonomously recognize the subtle differences in the diffraction patterns and output phase maps of the material. This study offers a path to machine learning coupled phase mapping as databases of EBSD patterns encompass an increasing number of the possible space groups, chemistry changes, and lattice parameter variations.

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“…On the other hand, CBED is limited by intense sample preparation, small areas of analysis, and substantial operator experience (Vecchio & Williams, 1987, 1988; Williams et al, 1991). In comparison, EBSD can be performed on large samples (Bernard et al, 2019; Hufford et al, 2019; Wang et al, 2019; Zhu et al, 2020), including three-dimensional EBSD (Calcagnotto et al, 2010), with high precision (~2°), high misorientation resolution (0.2°) and high spatial resolution (~40 nm) (Chen et al, 2011). Furthermore, the diffraction patterns collected in EBSD contain many of the same features observed in CBED, including excess and deficiency lines and higher-order Laue zone (HOLZ) rings (Michael & Eades, 2000; Winkelmann, 2008).…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Enabled Space Group Identification in EBSD

et al. 2020

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Electron backscatter diffraction (EBSD) is one of the primary tools in materials development and analysis. The technique can perform simultaneous analyses at multiple length scales, providing local sub-micron information mapped globally to centimeter scale. Recently, a series of technological revolutions simultaneously increased diffraction pattern quality and collection rate. After collection, current EBSD pattern indexing techniques (whether Hough-based or dictionary pattern matching based) are capable of reliably differentiating between a “user selected” set of phases, if those phases contain sufficiently different crystal structures. EBSD is currently less well suited for the problem of phase identification where the phases in the sample are unknown. A pattern analysis technique capable of phase identification, utilizing the information-rich diffraction patterns potentially coupled with other data, such as EDS-derived chemistry, would enable EBSD to become a high-throughput technique replacing many slower (X-ray diffraction) or more expensive (neutron diffraction) methods. We utilize a machine learning technique to develop a general methodology for the space group classification of diffraction patterns; this is demonstrated within the $\lpar 4/m\comma \;\bar{3}\comma \;\;2/m\rpar$ point group. We evaluate the machine learning algorithm's performance in real-world situations using materials outside the training set, simultaneously elucidating the role of atomic scattering factors, orientation, and pattern quality on classification accuracy.

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Design, fabrication and characterization of FeAl-based metallic-intermetallic laminate (MIL) composites

Cited by 37 publications

References 43 publications

Achieving room-temperature brittle-to-ductile transition in ultrafine layered Fe-Al alloys

Achieving room-temperature brittle-to-ductile transition in ultrafine layered Fe-Al alloys

Phase Mapping in EBSD Using Convolutional Neural Networks

Deep Neural Network Enabled Space Group Identification in EBSD

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