# Automated microscopy image analysis of sintered cBN materials

https://mdr.nims.go.jp/datasets/caa7dd61-8662-4c19-a06f-9c0648d27002

## Files

- [Automated microscopy image analysis of sintered cBN materials.pdf](https://mdr.nims.go.jp/filesets/dc8e4fd9-080a-4ca8-a8a0-7182583cb794/download) ([Detail](https://mdr.nims.go.jp/filesets/dc8e4fd9-080a-4ca8-a8a0-7182583cb794.md))

## Id

caa7dd61-8662-4c19-a06f-9c0648d27002

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-12-16T06:02:36.867354Z

## Updated at

2024-12-17T07:30:54.429346Z

## Published at

2024-12-17T07:30:54.497596Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2024.2423599

## Date published

2024-12-31

## Recorded date published

2024-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Automated microscopy image analysis of sintered cBN materials
  title_type: original
  lang: en

## Description

- description: Two protocols for multistep grain segmentation and analysis workflow
    in optical microscopy images of cubic boron nitride materials were developed and
    compared. One is based on statistical region merging and second one on morphological
    segmentation of grains without high contrast borders. Judging from corresponding
    manual image segmentation by expert, the second method gave more accurate grain
    boundaries and better statistical correspondence. Then, using the morphological
    segmentation method and incorporating of parameter optimization into it, a grain
    analysis workflow was established. Deviations from the correct answer (expert
    segmentation) were quantified based on five geometric statistical indices, and
    these deviations were added together to define the overall error. Cross-validation
    confirmed that the morphological segmentation workflow reproduces the expert segmentation
    with smaller 9.4% margin of error compared to 23.9% with statistical region merging
    one. The automated grain segmentation of such challenging materials with high
    throughput image analysis is an important help for industrial development of new
    milling tools.
  description_type: abstract
  lang: und

## Creator

- name: Dmitry S. Bulgarevich
  role: author
  orcid: https://orcid.org/0000-0002-7086-8396
- name: Moe Sakaguchi
  role: author
- name: Nobuyasu Nita
  role: author
- name: Masahiko Demura
  role: author
  orcid: https://orcid.org/0000-0002-7308-3041

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: metallurgy
  schema: not_defined
- subject: image analysis
  schema: not_defined
- subject: " microstructures"
  schema: not_defined
- subject: optical microscopy
  schema: not_defined
- subject: grain recognition
  schema: not_defined
- subject: cBN sintered compacts
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '4'
  issue: '1'

## Conference



## Related item



## Funding

- identifier: AG3030
  funder_name: "材料モデリング\tConstruction of Materials Fundamentals for Data-driven Research"
- identifier: AD2070
  funder_name: "積層材料\tStructural Materials for Extreme Environments toward a Carbon
    Neutral Society"
- identifier: GSG011
  funder_name: 三菱マテリアル／袖山慶太郎

## Instrument



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## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



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## Computational method



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## Fileset

- id: dc8e4fd9-080a-4ca8-a8a0-7182583cb794
  filename: Automated microscopy image analysis of sintered cBN materials.pdf
  content_type: application/pdf
  size: 15474187
  md5: 50c6674e2d8f12828acbc43d6700d6f6

## Thumbnail

fileset_id: dc8e4fd9-080a-4ca8-a8a0-7182583cb794
filename: Automated microscopy image analysis of sintered cBN materials.pdf