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説明:

To accelerate the design of multicomponent alloys, it is of key importance predicting required properties and identifying specific compositions from numerous element combinations. Prediction techniques, including theoretical strengthening models, numerical simulations, and machine learning approaches, offer promising avenues for accelerating alloy development. However, achieving high-precision predictions requires large experimental composition–process–structure–property (CPSP) datasets, which demand significant time and effort to acquire. This study introduces a high-throughput evaluation approach for generating composition–process–structure–high-temperature property datasets by combining diffusion couples with high-temperature nanoindentation. Applied to a Ni–Co binary alloy system containing face-centered cubic (fcc) and hexagonal close-packed (hcp) phases, this approach efficiently obtains 1144 data points, capturing the hardness across temperatures from 300 to 773 K and Co concentrations from 2 to 98 at%. These datasets facilitate data-driven analyses of empirical formulas for solid-solution strengthening.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Hardness, Nanoindentation, Diffusion couple, NI-Co alloy, High-throughput investigation

刊行年月日: 2025-09-18

出版者: Elsevier BV

掲載誌:

• Journal of Alloys and Compounds (ISSN: 09258388) vol. 1042 183868

研究助成金:

• 防衛装備庁 JPJ004596 (安全保障技術推進制度)

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1016/j.jallcom.2025.183868

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更新時刻: 2025-10-06 16:30:29 +0900

MDRでの公開時刻: 2025-10-06 16:21:49 +0900