# Prediction of continuous cooling transformation diagram for weld heat affected zone by machine learning

https://mdr.nims.go.jp/datasets/c5548153-dfa1-475b-ab9a-aee2b691d3b0

## File

- [Prediction of continuous cooling transformation diagram for weld heat affected zone by machine learning.pdf](https://mdr.nims.go.jp/filesets/92a0c1d3-7830-4b80-8bbc-e0a230dc117d/download) ([Detail](https://mdr.nims.go.jp/filesets/92a0c1d3-7830-4b80-8bbc-e0a230dc117d.md))

## Id

c5548153-dfa1-475b-ab9a-aee2b691d3b0

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-02-06T03:02:50.470114Z

## Updated at

2024-01-05T13:13:22.094929Z

## Published at

2023-02-08T07:18:57.187562Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2022.2123262

## Date published

2022-12-31

## Recorded date published

2022-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Prediction of continuous cooling transformation diagram for weld heat affected
    zone by machine learning
  title_type: original
  lang: en

## Description

- description: The continuous cooling transformation (CCT) diagram of steels is very
    important in considering the phase transformation depending on the cooling rate
    of a material; however, it is difficult to obtain the diagram for each steel because
    of much experimental effort required. Therefore, it is important to establish
    a technique to predict the CCT diagram with good accuracy under arbitrary conditions
    such as composition and cooling rate. We have developed a prediction model of
    a CCT diagram for the weld heat affected zone (HAZ) using machine learning based
    on existing experimental data. The prediction accuracy was improved by separately
    considering critical cooling rate and temperature at which the transformation
    starts at various cooling rates, and by using double cross-validation (DCV) to
    effectively use a small amount of data.
  description_type: abstract
  lang: eng

## Creator

- name: Satoshi Minamoto
  role: author
  orcid: https://orcid.org/0000-0003-4023-5800
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Susumu Tsukamoto
  role: author
  orcid: https://orcid.org/0000-0001-9011-2708
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Tadashi Kasuya
  role: author
- name: Makoto Watanabe
  role: author
  orcid: https://orcid.org/0000-0002-5064-9583
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Masahiko Demura
  role: author
  orcid: https://orcid.org/0000-0002-7308-3041
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher



## Managing organization



## Keyword

- subject: CCT
  schema: not_defined

## Rights



## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '2'
  issue: '1'
  start_page: 402
  end_page: 415

## Conference



## Related item



## Funding

- funder_name: JST
  description: SIP

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: 92a0c1d3-7830-4b80-8bbc-e0a230dc117d
  filename: Prediction of continuous cooling transformation diagram for weld heat
    affected zone by machine learning.pdf
  content_type: application/pdf
  size: 3257331
  md5: a6483beb9dab9eb0c97ec088898f601c

## Thumbnail

fileset_id: 92a0c1d3-7830-4b80-8bbc-e0a230dc117d
filename: Prediction of continuous cooling transformation diagram for weld heat affected
  zone by machine learning.pdf