TEM-based dislocation tomography: Challenges and opportunities

Feng, Zongqiang; Fu, Rui; Lin, Chengwei; Wu, Guilin; Huang, Tianlin; Zhang, Ling; Huang, Xiaoxu

doi:10.1016/j.cossms.2020.100833

Cited by 26 publications

(16 citation statements)

References 51 publications

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“…Until recently, the prevalent technique used to study dislocation patterning has been TEM, which resolves dislocations by imaging through thin foils (∼ 200nm). Studies of foils are not necessarily representative of the bulk because the dislocations' attraction to surfaces can alter their interactions [12,13]. Deformation-induced planar boundaries typically form along nearly parallel planes and extend over several tens of micrometers.…”

Section: Introductionmentioning

confidence: 99%

Extensive 3D Mapping of Dislocation Structures in Bulk Aluminum

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Extensive 3D Mapping of Dislocation Structures in Bulk Aluminum

et al. 2022

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“…We rst use the method like dislocation tomography 24,25 to reconstruct the distribution of those solar are tracks in three dimensions (detailed in Method), given that the contrast of the solar are tracks in TEM images can be maintained in a quite wide range of tilting angles. Figure 2b is the reconstructed image of the solar are tracks in Fig.…”

Section: Resultsmentioning

confidence: 99%

Atomic Delocalization in Solar Flare Heavy-Ion Tracks and Its Impact on the Plastic Deformation of CE-5 Lunar Soil

Chen

Fang

et al. 2023

Preprint

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Understanding the impact of space weathering on the mechanical properties of materials can provide strong implications for the exploration of the space including the building of a permanent base on airless planets. By examining the structure of solar flare tracks, which exist prevalently in lunar soils returned by Chang’e-5 mission, we revealed that the solar flare tracks are nanosized tubular defects where the silicon and oxygen atoms are delocalized. They are created by implantations of particles with atomic number larger than vanadium. The solar flare tracks first function as dislocation sources and act as strong hindrance for dislocation motions subsequently, resulting in dislocation multiplication and strain hardening. These changes make the mechanical properties of lunar soil significantly distinct from its counterpart residing on the Earth.

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“…Deep learning (DL) based methods can be very powerful tools for performing pixel wise classification to segment objects of interest. This can significantly help to automate the analysis of microscopic images [12][13][14]. In general, state of the art DL architectures such as the U-Net [15] have been found to be very successful for image segmentation with applications ranging from the classical field of computer vision [16][17][18] to medical imaging [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Deep learning of crystalline defects from TEM images: a solution for the problem of ‘never enough training data’

Govind,

Oliveros,

Dlouhy

et al. 2024

Mach. Learn.: Sci. Technol.

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Crystalline defects, such as line-like dislocations, play an important role for the performance and reliability of many metallic devices. Their interaction and evolution still poses a multitude of open questions to materials science and materials physics. In-situ TEM experiments can provide important insights into how dislocations behave and move. During such experiments, the dislocation microstructure is captured in form of videos. The analysis of individual video frames can provide useful insights but is limited by the capabilities of automated identification, digitization, and quantitative extraction of the dislocations as curved objects. The vast amount of data also makes manual annotation very time consuming, thereby limiting the use of Deep Learning-based, automated image analysis and segmentation of the dislocation microstructure.In this work, a parametric model for generating synthetic training data for segmentation of dislocations is developed. Even though domain scientists might dismiss synthetic training images sometimes as too artificial, our findings show that they can result in superior performance, particularly regarding the generalizing of the Deep Learning models with respect to different microstructures and imaging conditions. Additionally, we propose an enhanced deep learning method optimized for segmenting overlapping or intersecting dislocation lines. Upon testing this framework on four distinct real datasets, we find that our synthetic training data are able to yield high-quality results also on real images–even more so if fine-tune on a few real images was done. Our approach demonstrates the potential of synthetic data in overcoming the limitations of manual annotation in TEM, paving the way for more efficient and accurate analysis of dislocation microstructures. Last but not least, segmenting such thin, curvilinear structures is a task that is ubiquitous in many fields, which makes our method a potential candidate for other applications as well.

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TEM-based dislocation tomography: Challenges and opportunities

Cited by 26 publications

References 51 publications

Extensive 3D Mapping of Dislocation Structures in Bulk Aluminum

Extensive 3D Mapping of Dislocation Structures in Bulk Aluminum

Atomic Delocalization in Solar Flare Heavy-Ion Tracks and Its Impact on the Plastic Deformation of CE-5 Lunar Soil

Deep learning of crystalline defects from TEM images: a solution for the problem of ‘never enough training data’

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