# Deep neural network approach to estimate biaxial stress-strain curves of sheet metals

https://mdr.nims.go.jp/datasets/16102982-584f-4d35-899b-24333ca94956

## File

- [yamanaka_jmad2020.pdf](https://mdr.nims.go.jp/filesets/1de25afe-ec98-4d65-886e-13b92172bbdc/download) ([Detail](https://mdr.nims.go.jp/filesets/1de25afe-ec98-4d65-886e-13b92172bbdc.md))

## Id

16102982-584f-4d35-899b-24333ca94956

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-02-22T06:49:12.028406Z

## Updated at

2024-01-05T13:11:26.983799Z

## Published at

2023-02-28T04:13:24.088899Z

## Doi



## First published url

https://doi.org/10.1016/j.matdes.2020.108970

## Date published

2020-07-15

## Recorded date published

2020-10

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Deep neural network approach to estimate biaxial stress-strain curves of
    sheet metals
  title_type: original
  lang: en

## Description

- description: Multiaxial material testing is used to evaluate plastic deformation
    behavior of sheet metals in multiaxial stress states. A constitutive law calibrated
    by the results of multiaxial material tests can improve the accuracy of sheet
    metal forming simulations. However, this testing requires specialized equipment.
    This proof-of-concept study proposed and validated a deep neural network (DNN)
    approach to efficiently estimate biaxial stress-strain curves of aluminum alloy
    sheets from an image of the sample's crystallographic texture. The DNNs were trained
    using synthetic crystallographic texture datasets, and the corresponding biaxial
    stress-strain curves were generated by experimentally validated crystal plasticity
    finite element simulations, referred to as the numerical biaxial tensile tests.
    The synthetic crystallographic textures included preferred texture components
    typically found in aluminum alloys. The DNN based on two-dimensional convolutional
    neural networks (DNN-2D) accurately estimated the biaxial stress-strain curves
    from an {111} pole figure image. Further, the DNN comprising three dimensional
    convolutional neural networks (DNN-3D) provided a superior estimation of the biaxial
    stress-strain curves than DNN-2D using a three-dimensional image of the crystallographic
    texture. These findings demonstrated that the developed DNNs and their training
    procedures offer a new approach to efficiently provide virtual data for material
    modeling to enhance the accuracy of sheet metal forming simulations.
  description_type: abstract
  lang: eng

## Creator

- name: Akinori Yamanaka
  role: author
- name: Ryunosuke Kamijyo
  role: author
- name: Kohta Koenuma
  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: Elsevier BV

## Managing organization



## Keyword

- subject: Deep neural network
  schema: not_defined
- subject: Material modeling
  schema: not_defined
- subject: Multiaxial material testing
  schema: not_defined
- subject: Aluminum alloy sheets
  schema: not_defined

## Rights

- description: Creative Commons BY Attribution 4.0 International
  identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: MATERIALS & DESIGN
  issn: '02641275'
  volume: '195'
  start_page: 108970
  end_page: 108970

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## Fileset

- id: 1de25afe-ec98-4d65-886e-13b92172bbdc
  filename: yamanaka_jmad2020.pdf
  content_type: application/pdf
  size: 4926387
  md5: 2a7ec9c8366532d4cf0619875a1e0049

## Thumbnail

fileset_id: 1de25afe-ec98-4d65-886e-13b92172bbdc
filename: yamanaka_jmad2020.pdf