# Oxidation Properties of Additively Manufactured High Entropy Alloys: A Short Review

https://mdr.nims.go.jp/datasets/f684720b-069d-4547-89b9-50aa6222ee71

## File

- [240906Review Oxi.  AM HEAs_HTCPM2024_.docx](https://mdr.nims.go.jp/filesets/6aefb7e9-ee21-4083-94cd-e897513cd26d/download) ([Detail](https://mdr.nims.go.jp/filesets/6aefb7e9-ee21-4083-94cd-e897513cd26d.md))

## Id

f684720b-069d-4547-89b9-50aa6222ee71

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-01-06T05:34:22.788533Z

## Updated at

2025-09-22T23:30:15.435615Z

## Published at

2025-09-22T23:20:54.756907Z

## Doi

https://doi.org/10.48505/nims.5247

## First published url

https://doi.org/10.1007/s11085-024-10313-3

## Date published

2024-09-23

## Recorded date published

2024-12

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: 'Oxidation Properties of Additively Manufactured High Entropy Alloys: A Short
    Review'
  title_type: original
  lang: en

## Description

- description: Refractory high-entropy alloys (RHEAs) and complex concentrated alloys
    (RCCAs) are vital for high-temperature applications beyond the capabilities of
    Ni-based superalloys. Traditional methods for predicting oxidation resistance
    in these alloys are often inaccurate and resource-intensive. This study introduces
    a novel approach using Gradient Boosted Decision Trees (GBDT), an artificial intelligence
    technique, to predict specific mass gain due to oxidation. Utilizing a dataset
    synthesized from extensive literature and characterized by diverse alloy compositions
    and oxidation conditions, the model was trained using Iterated Nested k-fold Cross
    Validation with Shuffling (INKCVS). Our findings demonstrate that the GBDT model
    achieves a good balance between accuracy and generalization capacity in predicting
    oxidation resistance, as validated experimentally with selected alloys. This approach
    not only enhances prediction accuracy but also significantly reduces the need
    for extensive experimental testing, facilitating rapid development of new high-performance
    materials.
  description_type: abstract
  lang: und

## Creator

- name: Jhuo-Lun Lee
  role: author
- name: An-Chou Yeh
  role: author
- name: Hideyuki Murakami
  role: author
  orcid: https://orcid.org/0000-0001-8220-5816
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: Oxidation properties
  schema: not_defined
- subject: Additive manufacturing
  schema: not_defined
- subject: High entropy alloys
  schema: not_defined

## Rights

- description: 'This version of the article has been accepted for publication, after
    peer review (when applicable) and is subject to Springer Nature’s AM terms of
    use, but is not the Version of Record and does not reflect post-acceptance improvements,
    or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11085-024-10313-3.'
  identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo

start_date: 2024-09-23
end_date: 2025-09-23

## Journal

- title: High Temperature Corrosion of Materials
  issn: '27318397'
  volume: '101'
  issue: '6'
  start_page: 1369
  end_page: 1379

## Conference



## Related item



## Funding

- funder_name: Ministry of Education (MOE) in Taiwan.
- identifier: 112-2927-I-007-504
  funder_name: National Science and Technology Council (NSTC) in Taiwan

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## Fileset

- id: 6aefb7e9-ee21-4083-94cd-e897513cd26d
  filename: 240906Review Oxi.  AM HEAs_HTCPM2024_.docx
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  size: 2878446
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## Thumbnail

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filename: 240906Review Oxi.  AM HEAs_HTCPM2024_.docx