Effect of Atomic Ordering of DO3 Precipitates on Superelasticiy and Functional Fatigue of Iron-Based Shape Memory Alloy FeMnAlNiTi

R. Sidharth, H. Akamine, W. Abuzaid, ASK. Mohammad, T. Niendorf, M. Nishida +1 · Springer Science and Business Media LLC · 2025

The study links atomic ordering of DO3 precipitates in FeMnAlNiTi shape memory alloy to enhanced superelasticity and functional fatigue resistance.

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Plain English summary

This research investigates the iron-based shape memory alloy FeMnAlNiTi, which shows significant superelastic properties over a wide temperature range. The study highlights the importance of nanoprecipitates in enhancing the alloy's functionality, particularly through the degree of ordering of these precipitates.

Why this matters

Understanding the relationship between atomic ordering and superelasticity in shape memory alloys can lead to improved materials for various applications in manufacturing. Enhanced functional fatigue resistance is crucial for the longevity and reliability of materials used in demanding environments.

Key findings

  • FeMnAlNiTi exhibits over 400 °C temperature window of superelasticity.
  • Degree of ordering of DO3 precipitates is linked to superelastic functionality.
  • Prolonged aging increases the order of precipitates without changing their size.
  • Ultrahigh transformation stress of about 1.4 GPa can be attained.
  • Functional fatigue resistance increases with the ordering of precipitates.

What's new

The study establishes a direct link between the atomic ordering of precipitates and the superelastic properties of the alloy.

Limitations

The abstract does not discuss practical applications or the commercial viability of the findings.

Commercial context

The research is still in the laboratory phase and does not indicate commercial availability.

Publication

Publisher
Springer Science and Business Media LLC
Publication date
September 1, 2025
Research type
Paper
License
https://creativecommons.org/licenses/by/4.0

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Method note: Summaries and ratings on this page are generated by AI from the abstract only. Read the original paper for full context. · Model: gpt-4o-mini-2024-07-18