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Related Experiment Video

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Fabrication of Carbon-Based Ionic Electromechanically Active Soft Actuators

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Self-Sensing Variable Stiffness Actuation of Shape Memory Coil by an Inferential Soft Sensor.

Bhagoji Bapurao Sul¹, Dhanalakshmi Kaliaperumal¹, Seung-Bok Choi^2,3

¹Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This study introduces self-sensing variable stiffness actuation (SSVSA) for shape memory alloys (SMAs). Researchers developed a Support Vector Machine (SVM) model to predict stiffness from electrical resistance, enabling sensor-less systems.

Keywords:

electrical resistance joule heating effect nonlinear regression model self-sensing actuation shape memory coil support vector machine regression model variable stiffness actuation

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Area of Science:

Materials Science
Robotics
Mechanical Engineering

Background:

Self-sensing actuation in shape memory alloys (SMAs) leverages internal electrical property changes to monitor mechanical and thermal variables.
Traditional stiffness sensing requires dedicated physical sensors, complicating system design and miniaturization.

Purpose of the Study:

To develop a method for predicting the stiffness of a shape memory coil (SMC) using its electrical resistance.
To simulate self-sensing capabilities for variable stiffness actuation (VSA) using a Support Vector Machine (SVM) regression model.

Main Methods:

Experimental evaluation of a passive biased SMC in antagonistic connection under varying electrical and mechanical conditions.
Measurement of instantaneous electrical resistance using a voltage division method.
Development and validation of an SVM regression model to predict stiffness from electrical resistance.

Main Results:

The SVM model accurately predicted stiffness, showing good correlation with experimentally determined stiffness.
Performance metrics like RMSE and goodness of fit validated the model's accuracy.
The proposed method successfully demonstrated self-sensing stiffness from electrical resistance.

Conclusions:

Self-sensing variable stiffness actuation (SSVSA) using SVM offers a promising approach to eliminate the need for physical stiffness sensors in SMA applications.
This technique enables sensor-less, miniaturized, and simplified control systems with potential for stiffness feedback control.