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Active Control of Nitinol-Reinforced Composite Beam

  • A. Baz
  • S. Poh
  • J. Ro
  • M. Mutua
  • J. Gilheany
Chapter
Part of the Solid Mechanics and Its Applications book series (SMIA, volume 13)

Abstract

Considerable attention has been devoted recently to the utilization of the Shape Memory NIckel-TItanium alloy (NITINOL) in developing SMART composites that are capable of adapting intelligently to external disturbances (Ikegami et al. 1990, Rogers et al.1991, and Baz et al. 1990 and 1991). Such wide acceptance of NITINOL stems from its unique behavior when it is subjected to particular heating and cooling strategies. For example, the alloy becomes soft when it is cooled below its martensite transformation temperature and becomes about four times stiffer when it is heated above its austenite transformation temperature (Funakubo 1987). Furthermore, if the alloy is trained to have a particular shape while in its austenite phase, it will memorize this shape. If the alloy is then cooled to its martensite phase and subject to plastic deformation, it will return to its memorized shape when it is heated above the austenite transformation temperature. The phase transformation from martensite to austenite produces significant forces as the alloy recovers its original shape. The alloy acts as an actuator transforming thermal energy into mechanical energy (Perkins 1975 and Duerig et al. 1990). Accordingly, if the NITINOL fibers are embedded inside a composite matrix at optimal locations, they can be used to control the static and dynamic characteristics of the resulting SMART composite. The control action is generated by the described stiffening of the NITINOL fibers and/or the shape memory effect. With such built-in control capabilities, the performance of the SMART composites can be optimized and tailored to match changes in operating conditions.

Keywords

Shape Memory Alloy Composite Beam Shape Memory Effect Controller Gain Beam Cross Section 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1992

Authors and Affiliations

  • A. Baz
    • 1
  • S. Poh
    • 1
  • J. Ro
    • 1
  • M. Mutua
    • 1
  • J. Gilheany
    • 1
  1. 1.Department of Mechanical EngineeringThe Catholic University of AmericaUSA

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