Engineering grain boundary anisotropy to elucidate grain growth behavior in alumina

Conry, Bryan; Harley, Joel B.; Tonks, Michael; Kesler, Michael S.; Krause, Amanda R.

doi:10.1016/j.jeurceramsoc.2022.06.059

Cited by 14 publications

(13 citation statements)

References 57 publications

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“…Calcia-doped alumina (Al 2 O 3 ) is employed as a model material for this investigation, as it is known to exhibit unique microstructural properties Rodel and Glaeser (1989); Akiva et al (2014); Conry et al (2022). To induce crystallographic texture in our microstructures, we use a magnetic slip-casting technique to induce significant crystallographic texture in the as-cast green body Suzuki et al (2006).…”

Section: Materials Fabrication and Characterizationmentioning

confidence: 99%

“…To induce crystallographic texture in our microstructures, we use a magnetic slip-casting technique to induce significant crystallographic texture in the as-cast green body Suzuki et al (2006). Details of this technique and the experimental procedure are described in a previous work Conry et al (2022). Briefly, two pellets of 300 ppm calcia-doped alumina were prepared by slip casting in and outside a 9 T magnetic field, respectively.…”

Section: Materials Fabrication and Characterizationmentioning

confidence: 99%

“…Across all heat treatments, a mean accuracy of 0.91 ± 0.03 is reported. All stages of grain growth can be classified with greater than 0.90 accuracy except for the 8 h heat treatment (0.85), which is attributed to the fact that the 8 h textured microstructure is the least textured of all heat treatment durations Conry et al (2022). Overall, there is no apparent accuracy trend with heat treatment duration, indicating that the high-order experimentally indeterminate feature learned by the CNN is equally present at both early stages of grain growth, before morphological differences are more apparent, and later stages of grain growth.…”

Section: Classification At Individual Stages Of Grain Growthmentioning

confidence: 99%

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Automated, high-accuracy classification of textured microstructures using a convolutional neural network

Khurjekar

Conry²,

Kesler³

et al. 2023

Front. Mater.

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Crystallographic texture is an important descriptor of material properties but requires time-intensive electron backscatter diffraction (EBSD) for identifying grain orientations. While some metrics such as grain size or grain aspect ratio can distinguish textured microstructures from untextured microstructures after significant grain growth, such morphological differences are not always visually observable. This paper explores the use of deep learning to classify experimentally measured textured microstructures without knowledge of crystallographic orientation. A deep convolutional neural network is used to extract high-order morphological features from binary images to distinguish textured microstructures from untextured microstructures. The convolutional neural network results are compared with a statistical Kolmogorov–Smirnov tests with traditional morphological metrics for describing microstructures. Results show that the convolutional neural network achieves a significantly improved classification accuracy, particularly at early stages of grain growth, highlighting the capability of deep learning to identify the subtle morphological patterns resulting from texture. The results demonstrate the potential of a convolutional neural network as a tool for reliable and automated microstructure classification with minimal preprocessing.

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Section: Materials Fabrication and Characterizationmentioning

confidence: 99%

Section: Materials Fabrication and Characterizationmentioning

confidence: 99%

Section: Classification At Individual Stages Of Grain Growthmentioning

confidence: 99%

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Automated, high-accuracy classification of textured microstructures using a convolutional neural network

Khurjekar

Conry²,

Kesler³

et al. 2023

Front. Mater.

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“…Understanding and controlling microstructural evolution in metals and metallic alloys is one of the central tasks of materials science and engineering. The dynamics of grain growth and the evolution of grain shape in metallic microstructures strongly depends on the individual mobility of different grain boundaries (GBs), i.e., on the change of the interface energy with its structural parameters [2,3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Towards active learning: A stopping criterion for the sequential sampling of grain boundary degrees of freedom

Schmalofski¹,

Kroll²,

Dette³

et al. 2023

Preprint

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Many materials processes and properties depend on the anisotropy of the energy of grain boundaries, i.e. on the fact that this energy is a function of the five geometric degrees of freedom (DOF) of the grain boundaries. To access this parameter space in an efficient way and discover energy cusps in unexplored regions, a method was recently established, which combines atomistic simulations with statistical methods [1]. This sequential sampling technique is now extended in the spirit of an active learning algorithm by adding a criterion to decide when the sampling is advanced enough to stop. To this instance, two parameters to analyse the sampling results on the fly are introduced: the number of cusps, which correspond to the most interesting and important regions of the energy landscape, and the maximum change of energy between two sequential iterations. Monitoring these two quantities provides valuable insight into how the subspaces are energetically structured. The combination of both parameters provides the necessary information to evaluate the sampling of the 2D subspaces of grain boundary plane inclinations of even non-periodic, low angle grain boundaries. With a reasonable number of datapoints in the initial design, only a few sequential iterations already influence the accuracy of the sampling substantially and the new algorithm outperforms regular high-throughput sampling.

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“…14,15 Previously, we investigated grain growth in textured alumina, which was prepared by slip casting in a high magnetic field. 16 This processing route allowed us to directly compare growth of untextured and textured alumina because they were prepared with the same raw materials and processing parameters, except for the application of a 9 T magnetic field at room temperature for the texture specimens. Like previous simulations, the population of low-angle grain boundaries increased in the textured microstructure but did not change in the random microstructure.…”

Section: Introductionmentioning

confidence: 99%

The evolution of grain boundary energy in textured and untextured Ca‐doped alumina during grain growth

Conry,

Kole,

Burnett

et al. 2023

J Am Ceram Soc.

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The role of anisotropic grain boundary energy in grain growth is investigated using textured microstructures that contain a high proportion of special grain boundaries. Textured and untextured Ca‐doped alumina was prepared by slip casting inside and outside a high magnetic field, respectively. At 1600°C, the textured microstructure exhibits faster growth than the untextured microstructure and its population of low‐angle boundaries increases. Atomic force microscopy (AFM) is employed to measure the geometry of thermal grooves to assess the relative grain boundary energy of these systems before and after growth. In the textured microstructure, the grain boundary energy distribution narrows and shifts to a lower average energy. Conversely, the energy distribution broadens for the untextured microstructure as it grows and exhibits abnormal grain growth. Further analysis of the boundary networks neighboring abnormal grains reveals an energy incentive that facilitates their growth. These results suggest that coarsening is not the only dominant grain growth mechanism and that the system can lower its energy effectively by replacing high energy boundaries with those of low energy. The faster growth of lower energy boundaries suggests that isotropic simulations do not adequately account for anisotropic grain growth mechanisms or anisotropic mobility.This article is protected by copyright. All rights reserved

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Engineering grain boundary anisotropy to elucidate grain growth behavior in alumina

Cited by 14 publications

References 57 publications

Automated, high-accuracy classification of textured microstructures using a convolutional neural network

Automated, high-accuracy classification of textured microstructures using a convolutional neural network

Towards active learning: A stopping criterion for the sequential sampling of grain boundary degrees of freedom

The evolution of grain boundary energy in textured and untextured Ca‐doped alumina during grain growth

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