Microstructural classification of unirradiated LiAlO2 pellets by deep learning methods

Pazdernik, Karl; LaHaye, N. L.; Artman, Conor M.; Zhu, Yuanyuan

doi:10.1016/j.commatsci.2020.109728

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…Challenges related to capturing phenomena at nanosecond timescales in nanometer-length scales are of utmost importance to fully understand the coupled extreme environments discussed [44,45] with ongoing work focusing on the reliability and accessibility of such experiments. Real-time monitoring of electron beam effects and precise temperatures of materials in the experiments are also crucial for providing accurate inputs for computational models built from the data [46]. Given the progress of artificial intelligence and machine learning for materials science applications [47], it is well within grasp to have the tools necessary to synthesize rich datasets generated from these complex environments into well-understood mechanistic behaviors.…”

Section: Discussionmentioning

confidence: 99%

Exploring Coupled Extreme Environments via In-situ Transmission Electron Microscopy

et al. 2021

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Section: Discussionmentioning

confidence: 99%

Exploring Coupled Extreme Environments via In-situ Transmission Electron Microscopy

et al. 2021

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“…Quantifying the distribution of these fea-tures, such as the abundance of particular phases, defects, and morphology, is a common but challenging and time-consuming task. 9,29 We consider two very different examples: a cross-sectional thin film heterostructure of SrTiO 3 (STO) / Ge and nanoparticles of MoO 3 .…”

Section: E Usage Examplesmentioning

confidence: 99%

Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data

Doty,

Gallagher,

Cui

et al. 2021

Preprint

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The recent growth in data volumes produced by modern electron microscopes requires rapid, scalable, and flexible approaches to image segmentation and analysis.Few-shot machine learning, which can richly classify images from a handful of userprovided examples, is a promising route to high-throughput analysis. However, current command-line implementations of such approaches can be slow and unintuitive to use, lacking the real-time feedback necessary to perform effective classification.Here we report on the development of a Python-based graphical user interface that enables end users to easily conduct and visualize the output of few-shot learning models. This interface is lightweight and can be hosted locally or on the web, providing the opportunity to reproducibly conduct, share, and crowd-source few-shot analyses.

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“…Durmaz et al (2021) used DL to segment lath-shaped bainite in complex-phase steel microstructures, based on annotations from correlative EBSD data. Numerous examples for DL segmentation of grain or cell structures, across different domains, can also be found, e.g., (Konovalenko et al, 2018a;Konovalenko et al, 2018b;Bordignon et al, 2019;Furat et al, 2019;Pazdernik et al, 2020;Das et al, 2022). In a recent publication, the authors also applied DL to improve the automated PAG quantification based on new chemical etching on the basis of Bechet-Beaujard for improved delineation of PAG (Laubet al, Forthcoming 2022).…”

Section: Introductionmentioning

confidence: 99%

Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopy

Bachmann

Müller²,

Britz³

et al. 2022

Front. Mater.

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The high-temperature austenite phase is the initial state of practically all technologically relevant hot forming and heat treatment operations in steel processing. The phenomena occurring in austenite, such as recrystallization or grain growth, can have a decisive influence on the subsequent properties of the material. After the hot forming or heat treatment process, however, the austenite transforms into other microstructural constituents and information on the prior austenite morphology are no longer directly accessible. There are established methods available for reconstructing former austenite grain boundaries via metallographic etching or electron backscatter diffraction (EBSD) which both exhibit shortcomings. While etching is often difficult to reproduce and strongly depend on the investigated steel’s alloying concept, EBSD acquisition and reconstruction is rather time-consuming. But in fact, though, light optical micrographs of steels contrasted with conventional Nital etchant also contain information about the former austenite grains. However, relevant features are not directly apparent or accessible with conventional segmentation approaches. This work presents a deep learning (DL) segmentation of prior austenite grains (PAG) from Nital etched light optical micrographs. The basis for successful segmentation is a correlative characterization from EBSD, light and scanning electron microscopy to specify the ground truth required for supervised learning. The DL model shows good and robust segmentation results. While the intersection over union of 70% does not fully reflect the model performance due to the inherent uncertainty in PAG estimation, a mean error of 6.1% in mean grain size derived from the segmentation clearly shows the high quality of the result.

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Microstructural classification of unirradiated LiAlO2 pellets by deep learning methods

Cited by 15 publications

References 26 publications

Exploring Coupled Extreme Environments via In-situ Transmission Electron Microscopy

Exploring Coupled Extreme Environments via In-situ Transmission Electron Microscopy

Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data

Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopy

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