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Effect of Y2O3, TiO2, ZrO2 dispersion on oxidation resistance of W–Ni–Nb–Mo alloys

  • Atiqur Rahman Khan , Sambit Swain , Anshuman Patra ORCID logo EMAIL logo , D. Arvindha Babu and Bhaskar Majumdar
Published/Copyright: January 3, 2025
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Abstract

1.0 wt.% Y2O3 (alloy SY), TiO2 (alloy ST), ZrO2 (alloy SZ), dispersed 79W–10Ni–5Nb–5Mo (in weight%) alloys have been produced by mechanical alloying with 5:1 ball to powder weight ratio (BPR) for 20 h at 300 rpm in toluene medium. The powders were consolidated by spark plasma sintering at 1,100 °C for 5 min and at 65 MPa pressure. Oxidation study of the consolidated alloys was carried out at 1,000 °C for 10 h to establish the sustainability at elevated temperature. The study shows that alloys SY, ST exhibit compressive residual stress and alloy SZ evidences tensile residual stress in the oxidized condition. The oxidation rate is significantly lower in alloy SY due to passive layer formation facilitated by Y2O3 and Ni. The oxidation kinetic study also suggests substantial reduction in rate constant in alloy SY. The present study also provides interesting inputs regarding development of oxidation resistant alloys for high temperature applications.


Corresponding author: Anshuman Patra, Department of Metallurgical and Materials Engineering, National Institute of Technology Rourkela, Rourkela, 769008, Odisha, India E-mail:

Acknowledgments

Support from FIST, DST regarding XRD study at NIT Rourkela is also acknowledged.

  1. Research ethics: Not Applicable.

  2. Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission. Mr. Atiqur Rahman Khan has written the manuscript, Mr. Sambit Swain helped in editing and revising the manuscript, Dr. A. Patra supervised the research work and done editing, Dr. D. Arvindha Babu has assisted in analysis and revision, Dr. Bhaskar Majumdar has also assisted in analysis and revision.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: The research has been funded by ARMREB (DRDO) (project number: ARMREB/MAA/2017/196).

  7. Data availability: All data generated or analyzed during this study are included in this published article and its supplementary information files.

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Received: 2024-02-15
Accepted: 2024-06-12
Published Online: 2025-01-03
Published in Print: 2024-11-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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