Design and Performance Evaluation of Washing Machine Damper Using Shape Memory Alloy

Author(s):  
Chulhee Han ◽  
Tae-Hoon Lee ◽  
Jin-Hee An ◽  
Seung-Bok Choi
2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Blayton Padasdao ◽  
Bardia Konh

Abstract Today, several medical diagnosis and therapeutic cancer interventions are performed using needles via percutaneous surgical procedures. The success of these procedures highly depends on accurate placement of the needle tip at target positions. Improving targeting accuracy necessitates improvements in medical imaging and needle steering techniques. The former provides an improved vision on the target (i.e., cancerous tissue) and the needle, while the latter enables an enhanced interventional tool. In spite of considerable advancements in the medical imaging field, structure of the needle itself has remained unchanged. In the past decade, research works have suggested passive or active navigation of the needle inside the tissue to improve targeting accuracy. In addition, to provide actuation and control for needle steering, an active needle has been introduced activated by shape memory alloy (SMA) actuators. However, actuation of SMAs is complex due to their nonlinear and hysteresis behavior that depends on stress, strain, and temperature during operation. This work studies rapid manufacturing (via 3D printing), precise assembly, and performance evaluation of multiple distributed SMA actuators in an active flexible needle. The interactive response of the SMA actuators was investigated using experimental tests, constitutive material model, and kinematics of the active needle. It was shown that with proper installation of SMA actuators on the active needle, an effective manipulation can be realized in three dimensions.


Author(s):  
Edilberto Alves de Abrantes Júnior ◽  
Augusto Figueiredo ◽  
Carlos Jose de Araujo ◽  
Raimundo Duarte

Author(s):  
Md Mehedi Hasan ◽  
Theocharis Baxevanis

Shape Memory Alloy (SMA)-actuators are efficient, simple, and robust alternatives to conventional actuators when a small volume and/or large force and stroke are required. The analysis of their failure response is critical for their design in order to achieve optimum functionality and performance. Here, (i) the existing knowledge base on the fatigue and overload fracture response of SMAs under actuation loading is reviewed regarding the failure micromechanisms, empirical relations for actuation fatigue life prediction, experimental measurements of fracture toughness and fatigue crack growth rates, and numerical investigations of toughness properties and (ii) future developments required to expand the acquired knowledge, enhance the current understanding, and ultimately enable commercial applications of SMA-actuators are discussed.


Author(s):  
Mohammad R. Zakerzadeh ◽  
Mohsen Firouzi ◽  
Hassan Sayyaadi ◽  
Saeed Bagheri Shouraki

In systems with hysteresis behavior like Shape Memory Alloy (SMA) actuators and Piezo actuators, an accurate modeling of hysteresis behavior either for performance evaluation and identification or controller design is essentially needed. One of the most interesting hysteresis none-linearity identification methods is Preisach model which the hysteresis is modeled by linear combination of hysteresis operators. In spite of good ability of the Preisach model to extract the main features of system with hysteresis behavior, due to its numerical nature, it is not convenient to use in real time control applications. In this paper a novel artificial neural network (ANN) approach based on the Preisach model is presented which provides accurate hysteresis none-linearity modeling. It is shown that the proposed approach can represent hysteresis behavior more accurately in compare with the classical Preisach model and can be used for many applications such as hysteresis non-linearity control, hysteresis identification and realization for performance evaluation in some physical systems such as magnetic and SMA materials. It is also greatly decrease the extremely large amount of calculation needed to numerically implement the Preisach hysteresis model. For evaluation of the proposed approach an experimental apparatus consists of one-dimensional flexible aluminum beam actuated with a SMA wire is used. It is shown that the proposed ANN based Preisach model can identify hysteresis none-linearity more accurately than the classical Preisach model besides to its reduction in the simulation and computation time.


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