SMA Self-Sensing Actuator Training Using Impedance and Temperature
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Solution Overview
Problem
Soft actuators made from shape memory alloys (SMAs) face challenges in controlling force and length due to their sensitivity to temperature and environmental conditions, and the addition of external sensors degrades their performance.
Innovation Solution
A method and apparatus for training an artificial intelligence model to self-sense changes in length and forces generated by an SMA actuator in real-time, minimizing the need for external sensors by using impedance and temperature data for prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external sensors are attached to measure actuator length and force, then measurement precision is improved, but device complexity and weight increase
Solution Approach 1:
The SMA actuator measures its own length and force using its inherent impedance characteristics without requiring external sensors. The actuator serves its own measurement function by utilizing its electrical impedance, which naturally varies with its mechanical state, thereby eliminating the need for separate sensing components.
Solution Approach 2:
The patent replaces mechanical sensing systems (external sensors) with an electrical measurement approach. By measuring the electrical impedance of the SMA actuator, which changes with its mechanical state (length and force), the system substitutes complex mechanical sensors with simpler electrical measurements.
2Measurement precision
If external sensors are attached to measure actuator parameters, then measurement precision is improved, but output force relative to system weight decreases
Solution Approach 1:
The SMA actuator measures its own length and force using its inherent impedance characteristics without requiring external sensors. The actuator serves its own measurement function by utilizing its electrical impedance, which naturally varies with its mechanical state, thereby eliminating the need for separate sensing components.
Solution Approach 2:
The patent replaces mechanical sensing systems (external sensors) with an electrical measurement approach. By measuring the electrical impedance of the SMA actuator, which changes with its mechanical state (length and force), the system substitutes complex mechanical sensors with simpler electrical measurements.
3Reliability
If external sensors are attached to measure actuator parameters, then reliability is improved, but device complexity increases
Solution Approach 1:
The SMA actuator measures its own length and force using its inherent impedance characteristics without requiring external sensors. The actuator serves its own measurement function by utilizing its electrical impedance, which naturally varies with its mechanical state, thereby eliminating the need for separate sensing components.
Solution Approach 2:
The patent replaces mechanical sensing systems (external sensors) with an electrical measurement approach. By measuring the electrical impedance of the SMA actuator, which changes with its mechanical state (length and force), the system substitutes complex mechanical sensors with simpler electrical measurements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time self-sensing of SMA actuator performance with reduced sensor attachment, improving control efficiency and maintaining high output force relative to system weight.
Implementation Method 1
SMAs change their shape by a phase transformation between the austenite and martensite according to temperature
Implementation Method 2
SMAs change their shape by a phase transformation between the austenite and martensite according to temperature. The force and strain of SMA change sensitively to the surrounding environment or the conditions of an applied current
Implementation Method 3
a method for training an artificial intelligence model capable of predicting the changes in the SMA's length and the generated force of the actuator from impedance and temperature
Data Source
AI summary
In an apparatus and method for training artificial intelligence model for self-sensing actuator, the method includes acquiring a dataset from a shape memory alloy system by changing control parameters for controlling the shape memory alloy system, classifying the dataset into input data and ground truth data and labeling the dataset based on the ground truth data, and performing supervised training on the artificial intelligence model using the labeled dataset so that the artificial intelligence model outputs a generated force of a shape memory alloy with a specific shape or a length change of the shape memory alloy with the specific shape from the input data.