Electrical resistivity-based study of self-sensing properties for shape memory alloy-actuated artificial muscle

Area of Science:

• Materials Science
• Robotics
• Biomedical Engineering

Background:

• Shape memory alloys (SMAs) offer high power-to-weight ratios, flexibility, and silent operation, making them ideal for artificial muscles (AMs).
• Integrating self-sensing capabilities into SMAs can enhance their functionality for complex applications.

Purpose of the Study:

• To explore and model the self-sensing properties of SMAs.
• To develop an integrated actuation and self-sensing system for SMA-based artificial muscles (SMA-AMs).

Main Methods:

• An electrical resistivity (ER) transformation kinetics model was developed using simulated differential scanning calorimetry (DSC) data.
• Thermal-electrical-mechanical experiments were conducted to validate the ER model under various stress conditions.
• The self-sensing capability was demonstrated in a novel SMA-AM-actuated active ankle-foot orthosis (AAFO).

Main Results:

• The proposed ER model accurately reflects SMA transformation kinetics.
• The SMA-AM demonstrated reliable self-sensing functionality under different applied stresses.
• The integrated SMA-AM system was successfully applied to an AAFO, showcasing its practical potential.

Conclusions:

• SMA electrical resistivity can be effectively modeled to achieve self-sensing capabilities.
• This integrated approach enables SMA-AMs to perform both actuation and sensing functions.
• The developed SMA-AM technology shows promise for advanced assistive devices like AAFOs.