# Stress–strain curve predictions by crystal plasticity simulations and machine learning

https://mdr.nims.go.jp/datasets/4f8eea6e-05cd-4536-897a-271eada99b9a

## File

- [Stress–strain curve predictions.pdf](https://mdr.nims.go.jp/filesets/12845492-5ede-4753-93f0-b75ab5904be2/download) ([Detail](https://mdr.nims.go.jp/filesets/12845492-5ede-4753-93f0-b75ab5904be2.md))

## Id

4f8eea6e-05cd-4536-897a-271eada99b9a

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-12-16T05:30:28.672236Z

## Updated at

2024-12-17T23:30:38.891453Z

## Published at

2024-12-17T23:30:39.010569Z

## Doi



## First published url

https://doi.org/10.1038/s41598-024-80098-7

## Date published

2024-11-27

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Stress–strain curve predictions by crystal plasticity simulations and machine
    learning
  title_type: original
  lang: en

## Description

- description: The stress–strain curve (SSC) prediction for additively manufactured
    as-build metal materials with laser powder bed fusion (LPBF) is a lengthy and
    tedious process. It involves the sophisticated representative volume element (RVE)
    reconstruction of complex experimental microstructures for subsequent state-of-the-art
    crystal plasticity simulations with hyperparameter tunings in the appropriate
    physical model. However, even with a well-fitted model, simulations with different
    RVEs or temperatures, for example, are too time-consuming and computationally
    intensive. In recent years, several attempts were directed towards the SSC predictions
    with machine learning (ML) tools to speed up this process. Mainly, the artificial
    neural networks (ANN) were reported so far for this purpose. Here, we present
    our version to predict the temperature dependence of SSCs for LPBF fabricated
    industrially important Hastelloy X with various ML methods. Compared to previously
    reported studies on this matter with direct link between the microstructures and
    SSCs, we directly link only experimental conditions and predicted SSCs, which
    could be more preferable for some application scenarios discussed below. It was
    found that due to the structure and “small” size of our training dataset, the
    decision tree-based ML regressors worked better than other popular ML methods.
  description_type: abstract
  lang: und

## Creator

- name: Dmitry S. Bulgarevich
  role: author
  orcid: https://orcid.org/0000-0002-7086-8396
  organization: National Institute for Materials Science
- name: Makoto Watanabe
  role: author
  orcid: https://orcid.org/0000-0002-5064-9583
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: additive manufacturing
  schema: not_defined
- subject: crystal plasticity simulations
  schema: not_defined
- subject: representative volume element reconstruction
  schema: not_defined
- subject: machine learning regression
  schema: not_defined
- subject: stress–strain curve simulation/prediction
  schema: not_defined

## Rights

- description: 'Licensee: Springer Nature Limited Creative Commons licence CC BY-NC-ND:
    This licence permits any noncommercial use, sharing, distribution and reproduction
    in any medium or format, as long as appropriate credit is given to the original
    author(s) and the source, a link is provided to the Creative Commons licence,
    and any modification of the licensed material is indicated. Under this licence
    sharing of adapted material derived from this Article or parts of it is not allowed.
    Please read the full licence for further details at - http://creativecommons.org/licenses/by-nc-nd/4.0/'
  identifier: https://creativecommons.org/licenses/by-nc-nd/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Scientific Reports
  issn: '20452322'
  volume: '14'
  article_number: '29492'

## Conference



## Related item



## Funding

- identifier: EAD040
  funder_name: "3D積層造形プロセスのマルチフィジック\t-"
- identifier: AD2070
  funder_name: "積層材料\tStructural Materials for Extreme Environments toward a Carbon
    Neutral Society"

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## Specimen



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## Structure for specimen



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## Process for specimen treatment



## Computational method



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## Fileset

- id: 12845492-5ede-4753-93f0-b75ab5904be2
  filename: Stress–strain curve predictions.pdf
  content_type: application/pdf
  size: 7179600
  md5: adf4ae981bb2bf00fdfed59901a355f5

## Thumbnail

fileset_id: 12845492-5ede-4753-93f0-b75ab5904be2
filename: Stress–strain curve predictions.pdf