Ti-Ni-Cu shape memory alloy preparation by powder metallurgy and hot forging

Samah Elkhatib, Ayman Elsayed, Junko Umeda, Katsuyoshi Kondoh · Springer Science and Business Media LLC · 2026

Ti-Ni-Cu shape memory alloys show improved properties over traditional Ti-Ni alloys, making them suitable for advanced applications.

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Plain English summary

This study investigates the preparation of Ti-Ni-Cu shape memory alloys using powder metallurgy and hot forging. The researchers found that these alloys have higher transformation temperatures and improved corrosion resistance compared to traditional Ti-Ni alloys. The processing methods used, including spark plasma sintering and hot forging, significantly enhance the alloys' performance.

Why this matters

The development of Ti-Ni-Cu shape memory alloys could lead to better materials for applications requiring shape memory effects, such as in robotics or medical devices. Their improved properties, like corrosion resistance and hardness, make them valuable for advanced manufacturing.

Key findings

  • Ti-Ni-Cu SMAs show transformation temperatures of 60–75 °C.
  • Hot forging refines microstructure and improves performance.
  • Corrosion resistance is notably better than traditional Ti-Ni alloys.
  • Ti50-Ni30-Cu20 alloy exhibits a 28% increase in hardness.
  • The preparation route includes spark plasma sintering and heat treatment.

What's new

The study presents an optimized preparation route for Ti-Ni-Cu SMAs that enhances their functional and structural properties compared to traditional Ti-Ni alloys.

Limitations

The abstract does not specify the potential applications or the scalability of the production process.

Commercial context

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

Publication

Publisher
Springer Science and Business Media LLC
Publication date
March 26, 2026
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