Coupled Thermomechanical Modelling of Ni45Ti50Cu5 Shape Memory Alloy Actuators for High-Load Applications

Christoph Peukert, Stefan Heiland, Thomas Hutsch, Mario Scholze, Jens Müller, Ludwig Leser +2 · Springer Science and Business Media LLC · 2026

This research presents a thermomechanical model for high-load shape memory alloy actuators, enhancing their precision in industrial applications.

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Plain English summary

This study develops a comprehensive model for high-load actuators made from shape memory alloys (SMAs), specifically Ni45Ti50Cu5. These actuators are designed for precise control in industrial settings, such as machine tools, but face challenges due to nonlinear material behavior and thermal inertia during operation. The model incorporates a state variable for martensite fraction to better predict the phase transformation and its effects on actuator performance.

Why this matters

The ability to accurately control high-load actuators is crucial for improving efficiency and precision in industrial applications. By addressing the limitations of current SMA technologies, this research could lead to advancements in automation and manufacturing processes, benefiting various industries.

Key findings

  • Developed a comprehensive thermomechanical model for Ni45Ti50Cu5 SMA actuators.
  • Introduced martensite fraction as a state variable to describe phase transformation hysteresis.
  • Validated predictions with thermographic measurements.
  • Identified key thermomechanical properties experimentally.
  • Model applicable to various high-load actuator configurations.

What's new

The introduction of a state variable for martensite fraction and the coupling of thermal and mechanical models for high-load SMA actuators.

Limitations

The abstract does not specify the range of actuator configurations that can be applied or the extent of experimental validation.

Commercial context

The research addresses practical challenges in actuator design, indicating potential for industrial application, though further development is needed.

Publication

Publisher
Springer Science and Business Media LLC
Publication date
June 1, 2026
Research type
Paper
License
https://creativecommons.org/licenses/by/4.0

Tags

Applications
Industries

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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