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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.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Refractory high entropy alloys, Oxidation resistance, AI model

Date published: 2024-10-01

Publisher: Elsevier BV

Journal:

• SCRIPTA MATERIALIA (ISSN: 13596462) vol. 255 116394

Funding:

• Centre National de la Recherche Scientifique
• Agence Nationale de la Recherche ANR-22-PEXD-0003

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

MDR DOI:

First published URL: https://doi.org/10.1016/j.scriptamat.2024.116394

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Updated at: 2024-10-09 08:30:56 +0900

Published on MDR: 2024-10-09 08:30:56 +0900