# Classification of EBSD Kikuchi patterns for stainless steel by unsupervised learning methods to investigate grain boundaries

https://mdr.nims.go.jp/datasets/1dd7c318-6535-4be8-aef2-af51d3aded87

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- [2023_Aoyagi_eJSSNT.pdf](https://mdr.nims.go.jp/filesets/eec99a66-5b72-4826-bb1d-a76b4b362003/download)

## Id

1dd7c318-6535-4be8-aef2-af51d3aded87

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-10-20T14:05:06.144096Z

## Updated at

2024-01-05T13:11:33.851156Z

## Published at

2023-10-23T04:30:06.211559Z

## Doi



## First published url

https://doi.org/10.1380/ejssnt.2023-023

## Date published

2023-02-25

## Recorded date published

2023

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Classification of EBSD Kikuchi patterns for stainless steel by unsupervised
    learning methods to investigate grain boundaries
  title_type: original
  lang: en

## Description

- description: "EBSD indexing based on Kikuchi diffraction patterns, which indicate
    the types and orientation of the crystal lattice, is generally effective for characterizing
    crystals. Most regions in a sample can be indexed owing to the simulation of diffraction
    patterns of possible crystal types, orientations, and angles. However, indexing
    some of the complex regions related to the grain boundaries, dislocations, and
    strain areas is difficult. \r\nBy analyzing all the Kikuchi patterns, subtle information
    from mixed crystal conditions can be extracted. In this study, all Kikuchi patterns
    at all pixels in a measurement area of stainless steel were analyzed simultaneously
    using unsupervised learning methods, such as principal component analysis and
    multivariate curve resolution, and the pixels of the measurement area were classified
    based on the Kikuchi patterns to investigate the grain boundaries and dislocations
    in detail."
  description_type: abstract
  lang: eng

## Creator

- name: Satoka Aoyagi
  role: author
- name: Daisuke Hayashi
  role: author
- name: Yoshiharu Murase
  role: author
  orcid: https://orcid.org/0000-0001-7390-851X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Naoya Miyauchi
  role: author
  orcid: https://orcid.org/0000-0002-7716-3049
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Akiko N. Itakura
  role: author
  orcid: https://orcid.org/0000-0001-5783-141X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: Surface Science Society Japan

## Managing organization



## Keyword

- subject: hydrogen permeation
  schema: not_defined
- subject: EBSD
  schema: not_defined
- subject: stainless steel
  schema: not_defined
- subject: unsupervised learning
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc/4.0/

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: e-Journal of Surface Science and Nanotechnology
  issn: '13480391'
  volume: '21'
  issue: '3'
  start_page: 128
  end_page: 131

## Conference



## Related item



## Funding

- identifier: 18H03849
  funder_name: JSPS

## Instrument



## Instrument operator



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## Measurement method



## Specimen



## Chemical composition



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## Fileset

- id: eec99a66-5b72-4826-bb1d-a76b4b362003
  filename: 2023_Aoyagi_eJSSNT.pdf
  content_type: application/pdf
  size: 1098502
  md5: ce3102102a2ecb719b6b226bb05648b5

## Thumbnail

fileset_id: eec99a66-5b72-4826-bb1d-a76b4b362003
filename: 2023_Aoyagi_eJSSNT.pdf