Precision positioning using a novel six axes compliant nano-manipulator

In this paper, a novel micro-scale nano-manipulator capable of positioning in six degrees of freedom (DOF) is introduced. Undesired deflections, while operating in a specific DOF, are restricted by the aid of distinctive design of flexure hinges and actuators' arrangements. The compliant mechan...

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Bibliographic Details
Main Authors: Akbari, Samin (Contributor), Pirbodaghi, Tohid (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor)
Format: Article
Language:English
Published: Springer Berlin Heidelberg, 2016-12-22T15:11:50Z.
Subjects:
Online Access:Get fulltext
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100 1 0 |a Akbari, Samin  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Mechanical Engineering  |e contributor 
100 1 0 |a Akbari, Samin  |e contributor 
100 1 0 |a Pirbodaghi, Tohid  |e contributor 
700 1 0 |a Pirbodaghi, Tohid  |e author 
245 0 0 |a Precision positioning using a novel six axes compliant nano-manipulator 
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856 |z Get fulltext  |u http://hdl.handle.net/1721.1/105932 
520 |a In this paper, a novel micro-scale nano-manipulator capable of positioning in six degrees of freedom (DOF) is introduced. Undesired deflections, while operating in a specific DOF, are restricted by the aid of distinctive design of flexure hinges and actuators' arrangements. The compliant mechanism is actuated by thermo-electro-mechanical actuators, as they could be integrated and exert large forces in a nanometer resolution. The actuators are bidirectional capable of applying force in both transverse and longitudinal directions. Performance of the two degrees of freedom actuator is thoroughly explored via numerical and analytical analyses, showing a good agreement. The workspace and performance of the precision positioner is studied using finite element methods. Finally, identification of forward and inverse kinematic of the nano-manipulator is performed utilizing neural network concept. A well-trained and appropriate neural network can efficiently replace the time-consuming and complex analytical and experimental methods. 
546 |a en 
655 7 |a Article 
773 |t Microsystem Technologies