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Artificial Intelligence Approach to Predict Elevated Temperature Cyclic Oxidation of Fe–Cr and Fe–Cr–Ni Alloys

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Abstract

Predictive modelling of cyclic oxidation (CO) behavior is challenging. CO is dependent on several factors including time, temperature, atmosphere (composition, pressure) and alloy composition. The machine learning algorithm CatBoost is used to model, for the first time, CO of binary Fe-(10–20)% Cr alloys and ternary Fe-(16–20) %Cr-(10–30)%Ni alloys using published data. The CO conditions were 650 °C and 800 °C under air + 10% water vapor atmosphere. The CatBoost model was successfully trained and tested using 80:20% of the data with the composition, temperature and cycle time as input variables and mass change as the output. The five-fold cross-validation showed that the model had an average accuracy of 0.98 (R2). The CatBoost algorithm was also used as a classifier that, given a composition, predicts if the alloy will form protective oxide scale, oxidize rapidly or undergo spallation after 100 h of CO, accurately.

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Data Availability

The raw data have been extracted from [5]. Our curated data can be shared based on an appropriate request from any researcher.

Code Availability

The CatBoost model code is available here: https://github.com/sree369nidhi/Artificial-Intelligence-approach-to-predict-elevated-temperature-cyclic-oxidation-of-Fe-Cr-and-Fe-Cr.git

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Authors and Affiliations

  1. Intense Technologies Limited, Secunderabad, Telangana, 500009, India

    M. K. Anirudh

  2. Department of Computer Science, University of Southern California, Los Angeles, CA, 90007, USA

    M. Sreenidhi Iyengar

  3. Tata Consultancy Services, Hyderabad, 500081, India

    P. H. Anantha Desik

  4. Department of Metallurgical and Materials Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, 500075, India

    M. P. Phaniraj

Authors
  1. M. K. Anirudh
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  2. M. Sreenidhi Iyengar
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  3. P. H. Anantha Desik
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  4. M. P. Phaniraj
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Contributions

MKA contributed to formal analysis, software, methodology. MSI contributed to formal analysis, software. PHAD contributed to project administration. MPP contributed to conceptualization, data curation, writing—original draft, review and editing.

Corresponding author

Correspondence to M. P. Phaniraj.

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Anirudh, M.K., Iyengar, M.S., Desik, P.H.A. et al. Artificial Intelligence Approach to Predict Elevated Temperature Cyclic Oxidation of Fe–Cr and Fe–Cr–Ni Alloys. Oxid Met 98, 291–303 (2022). https://doi.org/10.1007/s11085-022-10123-5

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  • DOI: https://doi.org/10.1007/s11085-022-10123-5

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