Studying GPI zones in Al-Zn-Mg alloys by 4D-STEM

Thronsen, Elisabeth; Frafjord, Jonas; Friis, Jesper; Marioara, Calin Daniel; Wenner, Sigurd; Andersen, Sigmund Jarle; Holmestad, Randi

doi:10.1016/j.matchar.2021.111675

Cited by 29 publications

(10 citation statements)

References 31 publications

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“…Earlier works on Al-Zn-Mg alloys have shown that the GPI zones nucleated after prolonged NA or in the early stages of AA most often consisted of connected TCOs rather than single TCOs [13,37]. In the present study this is only verified for the NA 3 months condition of the Cu-free 7003 alloy.…”

Section: Discussionsupporting

confidence: 65%

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Effect of Cyclic Ageing on the Early-Stage Clustering in Al-Zn-Mg(-Cu) Alloys

Shah¹,

Thronsen²,

Wenner³

et al. 2022

SSRN Journal

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Section: Discussionsupporting

confidence: 65%

“…Most probably, a mixture of buried clusters and randomly aggregated solutes is present. The clusters that do exhibit ordering were all based on the same TCO structural units previously found in conventional NA samples [13,37].…”

Section: Atomic Structure Of the Ca And Na Clusters By Temmentioning

confidence: 72%

Effect of Cyclic Ageing on the Early-Stage Clustering in Al-Zn-Mg(-Cu) Alloys

Shah¹,

Thronsen²,

Wenner³

et al. 2022

SSRN Journal

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“…Each colored box in Figure 3 represents a region in which the pixels were averaged to create a single input feature. While virtual imaging has been commonly utilized to understand 4D-STEM datasets (Shukla et al, 2018; Mehta et al, 2020; Thornsen et al, 2021), virtual apertures are traditionally applied by identifying reflections of interest within the maximum diffraction pattern. Instead, this representation is expanded by creating virtual apertures that cover the entirety of the detected radial rings, reducing information loss from biased placement of virtual apertures.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, advances in inclusive featurization of CBED patterns could expand the utility of this method to a wider variety of materials. While other featurization approaches have been demonstrated (Ophus, 2019; Rauch & Veron, 2019; Mehta et al, 2020; Mu et al, 2021; Thornsen et al, 2021), methods to evaluate the information carried in these representations has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning

Bruefach

Ophus

Scott

2022

Microscopy and Microanalysis

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Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.

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“…In deformed Al-Mg-Si materials, SPED indicates that β" precipitates are sheared several times in single steps on different planes by dislocations during deformation [8]. In Al-Mg-Zn alloys, SPED is used in combination with density functional theory to study fine details of GP zones [9]. These and other results obtained from SPED will be discussed in the presentation [10].…”

mentioning

confidence: 99%