Download file (4.39 MB)
Download as zip (4.18 MB)

Collection

Citation

Description:

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.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Hardness, Nanoindentation, Diffusion couple, NI-Co alloy, High-throughput investigation

Date published: 2025-09-18

Publisher: Elsevier BV

Journal:

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

Funding:

• 防衛装備庁 JPJ004596 (安全保障技術推進制度)

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

MDR DOI:

First published URL: https://doi.org/10.1016/j.jallcom.2025.183868

Related item:

Other identifier(s):

Contact agent:

Updated at: 2025-10-06 16:30:29 +0900

Published on MDR: 2025-10-06 16:21:49 +0900