# Estimation of Texture-Dependent Stress-Strain Curve and <i>r</i>-Value of Aluminum Alloy Sheet Using Deep Learning

https://mdr.nims.go.jp/datasets/8a323b45-aeeb-4558-afa7-4d350fea5c13

## File

- [koenuma_MaterTrans2020.pdf](https://mdr.nims.go.jp/filesets/ab26f677-2409-427c-8e76-9ec338397644/download) ([Detail](https://mdr.nims.go.jp/filesets/ab26f677-2409-427c-8e76-9ec338397644.md))

## Id

8a323b45-aeeb-4558-afa7-4d350fea5c13

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-02-22T06:46:51.703821Z

## Updated at

2024-01-05T13:12:43.490666Z

## Published at

2023-03-02T04:22:12.915592Z

## Doi



## First published url

https://doi.org/10.2320/matertrans.P-M2020853

## Date published

2020-12-01

## Recorded date published

2020

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Estimation of Texture-Dependent Stress-Strain Curve and <i>r</i>-Value of
    Aluminum Alloy Sheet Using Deep Learning
  title_type: original
  lang: en

## Description

- description: "Deformation of an aluminum alloy sheet which is affected by its underlying
    crystallographic texture has been widely studied using crystal plasticity finite
    element method (CPFEM). Numerical material test based on the CPFEM allows us to
    quantitatively estimate stress-strain curve and the Lankford value (r-value) of
    aluminum alloy sheets that depend on the texture. However, to utilize the numerical
    material test as a means of optimizing the texture to design aluminum alloys,
    the CPFEM is computationally expensive. In this study, we propose a methodology
    for rapidly estimating the stress-strain curve and r-value of aluminum alloy sheets
    using the deep learning with a neural network. We train the neural network with
    synthetic texture and stress-strain curves calculated by the numerical material
    test. To capture the feature of synthetic texture from an {111} pole figure image,
    the neural network incorporates the convolution neural network. Using the trained
    neural network, we can estimate the uniaxial stress-strain curve and the in-plane
    anisotropy of r-value for various textures which contain Cube and S components.
    The results demonstrate that the neural network\r\ntrained with the results of
    the numerical material test is a promising methodology for rapidly estimating
    the deformation of aluminum alloy sheets."
  description_type: abstract
  lang: eng

## Creator

- name: Kohta Koenuma
  role: author
- name: Akinori Yamanaka
  role: author
- name: Ikumu Watanabe
  role: author
  orcid: https://orcid.org/0000-0002-7693-1675
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Toshihiko Kuwabara
  role: author

## Contact agent



## Publisher

organization: Japan Institute of Metals

## Managing organization



## Keyword

- subject: texture
  schema: not_defined
- subject: tension test
  schema: not_defined
- subject: numerical analysis
  schema: not_defined
- subject: crystallite orientation
  schema: not_defined
- subject: flow stress
  schema: not_defined
- subject: deep learning
  schema: not_defined

## Rights

- description: In Copyright
  identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: MATERIALS TRANSACTIONS
  issn: '13459678'
  volume: '61'
  issue: '12'
  start_page: 2276
  end_page: 2283

## Conference



## Related item



## Funding



## 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: ab26f677-2409-427c-8e76-9ec338397644
  filename: koenuma_MaterTrans2020.pdf
  content_type: application/pdf
  size: 2964349
  md5: 458d4c196c739f94113003663dc0bd11

## Thumbnail

fileset_id: ab26f677-2409-427c-8e76-9ec338397644
filename: koenuma_MaterTrans2020.pdf