Combined approach to capture the evolution of oxidation of Nickel based superalloys using data driven approaches Article Swipe
Nikhil Khatavkar
,
Abhishek K. Singh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1103/physrevmaterials.8.053601
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1103/physrevmaterials.8.053601
Nickel-based superalloys are an exceptional class of materials that are indispensable for high-temperature applications in the aerospace and power sector industries worldwide. The prolonged application of these materials in a demanding environment is hindered by the increased oxidation rates and deformation due to mass gain at high temperatures and the presence of corrosive agents. Calculating the oxidation properties using experimental techniques is laborious and highly cost/time intensive, which presents a considerable challenge to reducing the oxidation in these materials. In this work, we establish an extensive database consisting of the specific mass gain due to oxidation (�m) and the parabolic oxidation rates (kp) of nickel-based superalloys spanning all the superalloy generations. Highly accurate machine learning (ML) models are developed to predict (�m) using artificial neural networks and tree-based XGBoost. The ML models are extended by unsupervised k means clustering to improve the accuracy of the models and generate insights on the composition-property linkages. Additionally, the ML model for kp developed utilizing XGBoost yields unprecedented results with errors of 0.04. The ML model is analyzed using the SHapely Additive exPlanations parameters to determine the effect of individual features on the model. Further, we employ a genetic algorithm-based approach utilizing the developed ML models to minimize the kp to improve the performance of the superalloys at high temperatures. The genetic algorithm-assisted optimization successfully yields several compositions for new Ni superalloys with up to 20 reduction in the kp. This work presents essential advances for accelerating the targeted discovery of new materials for highly specialized and demanding applications. © 2024 American Physical Society.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1103/physrevmaterials.8.053601
- OA Status
- green
- Cited By
- 5
- References
- 95
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396707663
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4396707663Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1103/physrevmaterials.8.053601Digital Object Identifier
- Title
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Combined approach to capture the evolution of oxidation of Nickel based superalloys using data driven approachesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-07Full publication date if available
- Authors
-
Nikhil Khatavkar, Abhishek K. SinghList of authors in order
- Landing page
-
https://doi.org/10.1103/physrevmaterials.8.053601Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://mpra.ub.uni-muenchen.de/85113/1/MPRA_paper_85113.pdfDirect OA link when available
- Concepts
-
Superalloy, Materials science, Cluster analysis, Nickel, Artificial neural network, Genetic algorithm, Artificial intelligence, Computer science, Machine learning, Metallurgy, AlloyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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95Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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