# Estimating the S-N Curve by Machine Learning Random Forest Method

https://mdr.nims.go.jp/datasets/4f674c5c-49ad-4d2e-8afa-98cfb4d162de

## File

- [Estimation the S-N Curve by Machine Learning Random Forest Method_Mater. Trans. 65(2024)428-433.pdf](https://mdr.nims.go.jp/filesets/0e3046f4-55f8-4101-8cb1-dd2ea2f5bac2/download) ([Detail](https://mdr.nims.go.jp/filesets/0e3046f4-55f8-4101-8cb1-dd2ea2f5bac2.md))

## Id

4f674c5c-49ad-4d2e-8afa-98cfb4d162de

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-04-03T00:51:12.584380Z

## Updated at

2024-12-27T07:30:58.468455Z

## Published at

2024-12-27T07:30:58.524621Z

## Doi



## First published url

https://doi.org/10.2320/matertrans.MT-Z2023006

## Date published

2024-04-01

## Recorded date published

2024

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Estimating the S-N Curve by Machine Learning Random Forest Method
  title_type: original
  lang: en

## Description

- description: Fatigue limit is well predicted by tensile strength or hardness, and
    the relationship is often analyzed by linear regression using the minimum squared
    approximation. However, the prediction of the number cycles to failure at a given
    stress amplitude, meaning the estimate of the S–N curve, has not been realized.
    Therefore, we aim to investigate the estimability of the S–N curve using the random
    forest method based on the data described in the NIMS fatigue data sheet. The
    random forest method is a machine learning algorithm and an ensemble learning
    algorithm that integrates weak learners of multiple decision tree models to improve
    generalization ability. It was clarified that the machine learning of the multiple
    decision tree model is excellent in fatigue limit prediction. The S–N curve can
    be accurately estimated by combining the prediction of fatigue limit and the number
    of cycles to failure at a given stress amplitude.
  description_type: abstract
  lang: eng

## Creator

- name: Nobuo Nagashima
  role: author
  orcid: https://orcid.org/0000-0003-3588-980X
  organization: National Institute for Materials Science
- name: Masao Hayakawa
  role: author
  orcid: https://orcid.org/0000-0001-5143-8350
  organization: National Institute for Materials Science
- name: Hiroyuki Masuda
  role: author
  orcid: https://orcid.org/0000-0003-2847-1616
  organization: National Institute for Materials Science
- name: Kotobu Nagai
  role: author
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Japan Institute of Metals

## Managing organization



## Keyword

- subject: fatigue
  schema: not_defined
- subject: high-cycle fatigue
  schema: not_defined
- subject: data-sheet
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: random forest method
  schema: not_defined

## Rights

- identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: MATERIALS TRANSACTIONS
  issn: '13475320'
  volume: '65'
  issue: '4'
  start_page: 428
  end_page: 433
  article_number: MT-Z2023006

## Conference



## Related item



## Funding

- identifier: 20K04170
  funder_name: 日本学術振興会
  description: 大地震を模擬した高速ひずみ速度の極低サイクル繰り返し変形による疲労損傷の解析

## 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: 0e3046f4-55f8-4101-8cb1-dd2ea2f5bac2
  filename: Estimation the S-N Curve by Machine Learning Random Forest Method_Mater.
    Trans. 65(2024)428-433.pdf
  content_type: application/pdf
  size: 2806447
  md5: fb868fd4b5ac461d5ef3d159168ab7ce

## Thumbnail

fileset_id: 0e3046f4-55f8-4101-8cb1-dd2ea2f5bac2
filename: Estimation the S-N Curve by Machine Learning Random Forest Method_Mater.
  Trans. 65(2024)428-433.pdf