# Analysis of artificial intelligence-discovered patterns and expert-designed aging patterns for 0.2 % proof stress in Ni-Al alloys with γ – γ' two-phase structure

https://mdr.nims.go.jp/datasets/937553cb-0cc4-4275-a4a8-2824d3c2a3dd

## Download

- [1-s2.0-S2949822825000826-main.pdf](https://mdr.nims.go.jp/filesets/a04fe7ad-23d0-42e3-b2ba-790b9f738755/download)

## Id

937553cb-0cc4-4275-a4a8-2824d3c2a3dd

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-11-07T07:04:20.065559Z

## Updated at

2025-11-10T03:30:25.254981Z

## Published at

2025-11-10T03:24:32.384094Z

## Doi



## First published url

https://doi.org/10.1016/j.nxmate.2025.100564

## Date published

2025-02-28

## Recorded date published

2025-7

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Analysis of artificial intelligence-discovered patterns and expert-designed
    aging patterns for 0.2 % proof stress in Ni-Al alloys with γ – γ' two-phase structure
  title_type: original
  lang: en

## Description

- description: この発表では、人工知能（AI）を用いて提案された非等温時効（NIA）パターンによるNi-Al二元合金（γ–γ'二相）の機械的強度向上に関する解析結果を紹介する。従来の等温時効と比較して、AIにより導かれたNIA条件の一部は優れた0.2％耐力を示した。本研究では計2823通りのNIA条件を評価し、うち173通りが従来法を上回る強度を示した。さらに、AIが発見した条件およびAIに着想を得た専門家設計条件を詳細に解析し、700℃での保持およびその連続回数（二回が最適）、非連続的加熱工程（最大五回）が強度向上に重要であることを明らかにした。
  description_type: abstract
  lang: und
- description: 'This study presents the comprehensive analysis of flexible non-isothermal
    aging (NIA) patterns discovered through artificial intelligence (AI) to improve
    the mechanical strength (0.2 % proof stress) in γ – γ’ two-phase, binary Ni-Al
    alloys. In our recent investigation, we found that the AI algorithm could propose
    aging patterns with superior strength compared to conventional isothermal aging
    heat treatment. In this current study, we continued our extensive exploration
    of AI methodologies, uncovering diverse patterns that also surpassed the isothermal
    aging benchmark. Remarkably, out of 2823 NIA schedules, we found 173 ones outperforming
    the isothermal aging benchmark. Furthermore, we conducted a detailed analysis
    of newly AI-discovered patterns and expert-designed patterns inspired by AI. We
    identified two critical factors for strength improvement: exposure at 700℃ and
    the number of consecutive 700 ℃ exposures (optimally set at two), alongside non-consecutive
    steps (up to five). The insights gained from these findings may demonstrate the
    potential of AI-driven approaches to yield ideas on how to achieve improved strength
    in Ni-Al alloys.'
  description_type: abstract
  lang: en

## Creator

- name: Vickey Nandal
  role: author
  orcid: https://orcid.org/0000-0002-6155-6630
  organization: National Institute for Materials Science
- name: Sae Dieb
  role: author
  orcid: https://orcid.org/0000-0002-8111-2009
  organization: National Institute for Materials Science
- name: Dmitry S. Bulgarevich
  role: author
  orcid: https://orcid.org/0000-0002-7086-8396
  organization: National Institute for Materials Science
- name: Toshio Osada
  role: author
  orcid: https://orcid.org/0000-0003-1539-9264
  organization: National Institute for Materials Science
- name: Toshiyuki Koyama
  role: author
- name: Satoshi Minamoto
  role: author
  orcid: https://orcid.org/0000-0003-4023-5800
  organization: National Institute for Materials Science
- name: Masahiko Demura
  role: author
  orcid: https://orcid.org/0000-0002-7308-3041
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Artificial intelligence
  schema: not_defined
- subject: Monte carlo tree search
  schema: not_defined
- subject: Non-isothermal aging
  schema: not_defined
- subject: Ni-Al alloys
  schema: not_defined
- subject: Pattern analysis
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Next Materials
  issn: '29498228'
  volume: '8'
  article_number: '100564'

## Conference



## Related item



## Funding

- funder_name: JST
  description: "The Council for Science, Technology and\r\nInnovation (CSTI), Cross-ministerial
    Strategic Innovation Promotion\r\nProgram (SIP), \"Structural Materials for Innovation\"
    and ”Materials Integration for Revolutionary Design System of Structural Materials.\""
- identifier: JPMXP1122684766
  funder_name: MEXT
  description: Data Creation and Utilization Type Material Research and Development
    Project

## 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: a04fe7ad-23d0-42e3-b2ba-790b9f738755
  filename: 1-s2.0-S2949822825000826-main.pdf
  content_type: application/pdf
  size: 6987263
  md5: 969da19625e762a3374c2972f1640a4b

## Thumbnail

fileset_id: a04fe7ad-23d0-42e3-b2ba-790b9f738755
filename: 1-s2.0-S2949822825000826-main.pdf