Construction of Finite Element Meshes for Polycrystal Grains Model from X-ray CT Image

Kobayashi, Masakazu; Matsuyama, Tomohiko; Kouno, Aya; Toda, Hiroyuki; Miura, Hiromi

doi:10.2320/matertrans.m2016260

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Moreover, the model should be verified using novel experimental techniques. However, the experimental justification for heterogeneous deformation structures is not practically easy because non-destructive 3D data on the dislocation density and misorientation with a sub-micrometer spatial accuracy are required, which would be likely enabled by exploiting focused pencil beam 3D-XRD [36,37].…”

Section: Prediction Of Heterogeneous Deformation Structurementioning

confidence: 99%

Advances in research on deformation and recrystallization for the development of high-functional steels

Ushioda

2020

Science and Technology of Advanced Materials

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In order to exploit full potential of materials, it is necessary to fundamentally control their microstructures through grain refinement and orientation. Deformation and recrystallization are means to control the microstructure. Some recent examples of research on deformation and recrystallization which make use of advanced analytical techniques and computational materials science are examined and current limitations are identified. Finally, the potential for future developments is considered with respect to the unresolved technical problems that must be addressed as part of the development of new steels. ARTICLE HISTORY

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Section: Prediction Of Heterogeneous Deformation Structurementioning

confidence: 99%

Advances in research on deformation and recrystallization for the development of high-functional steels

Ushioda

2020

Science and Technology of Advanced Materials

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Assessment of hydrogen embrittlement behavior in Al-Zn-Mg alloy through multi-modal 3D image-based simulation

Fujihara,

Toda,

Ebihara

et al. 2024

International Journal of Plasticity

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Deep learning for three-dimensional segmentation of electron microscopy images of complex ceramic materials

Hirabayashi,

Iga,

Ogawa

et al. 2024

npj Comput Mater

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The microstructure is a critical factor governing the functionality of ceramic materials. Meanwhile, microstructural analysis of electron microscopy images of polycrystalline ceramics, which are geometrically complex and composed of countless crystal grains with porosity and secondary phases, has generally been performed manually by human experts. Objective pixel-based analysis (semantic segmentation) with high accuracy is a simple but critical step for quantifying microstructures. In this study, we apply neural network-based semantic segmentation to secondary electron images of polycrystalline ceramics obtained by three-dimensional (3D) imaging. The deep-learning-based models (e.g., fully convolutional network and U-Net) by employing a dataset based on a 3D scanning electron microscopy with a focused ion beam is found to be able to recognize defect structures characteristic of polycrystalline materials in some cases due to artifacts in electron microscopy imaging. Owing to the training images with improved depth accuracy, the accuracy evaluation function, intersection over union (IoU) values, reaches 94.6% for U-Net. These IoU values are among the highest for complex ceramics, where the 3D spatial distribution of phases is difficult to locate from a 2D image. Moreover, we employ the learned model to successfully reconstruct a 3D microstructure consisting of giga-scale voxel data in a few minutes. The resolution of a single voxel is 20 nm, which is higher than that obtained using a typical X-ray computed tomography. These results suggest that deep learning with datasets that learn depth information is essential in 3D microstructural quantifying polycrystalline ceramic materials. Additionally, developing improved segmentation models and datasets will pave the way for data assimilation into operando analysis and numerical simulations of in situ microstructures obtained experimentally and for application to process informatics.

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Construction of Finite Element Meshes for Polycrystal Grains Model from X-ray CT Image

Cited by 3 publications

References 21 publications

Advances in research on deformation and recrystallization for the development of high-functional steels

Advances in research on deformation and recrystallization for the development of high-functional steels

Assessment of hydrogen embrittlement behavior in Al-Zn-Mg alloy through multi-modal 3D image-based simulation

Deep learning for three-dimensional segmentation of electron microscopy images of complex ceramic materials

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