Article 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

Vickey Nandal ORCID (National Institute for Materials Science) ; Sae Dieb SAMURAI ORCID (National Institute for Materials Science) ; Dmitry S. Bulgarevich SAMURAI ORCID (National Institute for Materials Science) ; Toshio Osada SAMURAI ORCID (National Institute for Materials Science) ; Toshiyuki Koyama ; Satoshi Minamoto SAMURAI ORCID (National Institute for Materials Science) ; Masahiko Demura SAMURAI ORCID (National Institute for Materials Science)

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Vickey Nandal, Sae Dieb, Dmitry S. Bulgarevich, Toshio Osada, Toshiyuki Koyama, Satoshi Minamoto, Masahiko Demura. 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. Next Materials. 2025, 8 (), 100564. https://doi.org/10.1016/j.nxmate.2025.100564

Description:

(abstract)

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:

(abstract)

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.

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Keyword: Artificial intelligence, Monte carlo tree search, Non-isothermal aging, Ni-Al alloys, Pattern analysis

Date published: 2025-02-28

Publisher: Elsevier BV

Journal:

  • Next Materials (ISSN: 29498228) vol. 8 100564

Funding:

  • JST (The Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), "Structural Materials for Innovation" and ”Materials Integration for Revolutionary Design System of Structural Materials.")
  • MEXT JPMXP1122684766 (Data Creation and Utilization Type Material Research and Development Project)

Manuscript type: Publisher's version (Version of record)

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First published URL: https://doi.org/10.1016/j.nxmate.2025.100564

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Updated at: 2025-11-10 12:30:25 +0900

Published on MDR: 2025-11-10 12:24:32 +0900

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