A highly wear resistant nanostructured bainitic steel with accelerated transformation kinetics

Rangasayee Kannan; Yiyu Wang; Tomas Grejtak; Bryan Lim; Peeyush Nandwana

doi:10.1016/j.jmrt.2025.02.192

A highly wear resistant nanostructured bainitic steel with accelerated transformation kinetics

Rangasayee Kannan, Yiyu Wang, Tomas Grejtak, Bryan Lim, Peeyush Nandwana

Research output: Contribution to journal › Article › peer-review

Abstract

A coupled Calculation of Phase Diagrams (CALPHAD), machine learning, and data mining approach was used to design a new, highly wear-resistant nanostructured bainitic steel. Arc melting of the designed compositions, dilatometry, and advanced microscopy indicate that the designed steel had a nanoscale dual-phase structure of ferrite and austenite (approximately 50 nm) with kinetics 7x faster for the onset of bainite and 2x faster for complete transformation. Under dry sliding conditions using the current state-of-the-art AISI 52100 bearing steel as the counter sample, the designed steel little to no wear, indicating its potential for applications in high-wear service conditions.

Original language	English
Pages (from-to)	6797-6803
Number of pages	7
Journal	Journal of Materials Research and Technology
Volume	35
DOIs	https://doi.org/10.1016/j.jmrt.2025.02.192
State	Published - Mar 1 2025

Funding

This research is sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy . Research was performed at the U.S. Department of Energy\u2019s Manufacturing Demonstration Facility, located at Oak Ridge National Laboratory . This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The authors acknowledge Kevin Hanson, Sarah Graham, and Andres Marquez Rossy for help with arc melting of ingots, heat treatments, and metallographic sample preparation.

Keywords

Alloy design
CALPHAD
Data mining
Superbainite
Wear

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10.1016/j.jmrt.2025.02.192

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abstract = "A coupled Calculation of Phase Diagrams (CALPHAD), machine learning, and data mining approach was used to design a new, highly wear-resistant nanostructured bainitic steel. Arc melting of the designed compositions, dilatometry, and advanced microscopy indicate that the designed steel had a nanoscale dual-phase structure of ferrite and austenite (approximately 50 nm) with kinetics 7x faster for the onset of bainite and 2x faster for complete transformation. Under dry sliding conditions using the current state-of-the-art AISI 52100 bearing steel as the counter sample, the designed steel little to no wear, indicating its potential for applications in high-wear service conditions.",

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AU - Kannan, Rangasayee

AU - Wang, Yiyu

AU - Grejtak, Tomas

AU - Lim, Bryan

AU - Nandwana, Peeyush

PY - 2025/3/1

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AB - A coupled Calculation of Phase Diagrams (CALPHAD), machine learning, and data mining approach was used to design a new, highly wear-resistant nanostructured bainitic steel. Arc melting of the designed compositions, dilatometry, and advanced microscopy indicate that the designed steel had a nanoscale dual-phase structure of ferrite and austenite (approximately 50 nm) with kinetics 7x faster for the onset of bainite and 2x faster for complete transformation. Under dry sliding conditions using the current state-of-the-art AISI 52100 bearing steel as the counter sample, the designed steel little to no wear, indicating its potential for applications in high-wear service conditions.

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A highly wear resistant nanostructured bainitic steel with accelerated transformation kinetics

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Keywords

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