Shape memory alloys (SMA) when subjected to deformation at low temperature can recover their original shape by heating above a temperature called Austenite transformation temperature. This original shape is sustained till the material is deformed again by an applied stress. This property makes the SMA a unique actuator, which doesn’t require any other components. Also, the material’s resistance changes with deformation. Thus the change in resistance can be used to sense the deformation, which eliminates the requirements of additional sensors. This can make the system more compact and reduce the cost. In our study, a binary Nickel-Titanium alloy is used as a rotary actuator. The actuation is controlled by adjusting the temperature through controlled joule heating by varying the electric current. The manipulator used in this research is a single degree-of-freedom, bias type actuator. SMA actuation in this system is under a varying stress, thereby creating a complex thermo-mechanical condition which affects the transformation temperatures, significantly. Also, the resistance change during heating and cooling paths exhibit hysteresis behavior. This paper investigates the use of artificial neural network (ANN) in establishing relationship between resistance and angular position of the manipulator. To model the hysteresis behavior of the SMA, in addition to resistance of the SMA, other electric properties like voltage etc are given as input to the ANN. The obtained ANN model is able to determine the angular position of the rotary manipulator with good accuracy.

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