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Target Shape Optimization of Functionally Graded Shape Memory Alloy Compliant Mechanism

[+] Author Affiliations
Jovana Jovanova

Ss. Cyril and Methodius University, Skopje, Macedonia

Mary Frecker, Reginald F. Hamilton, Todd A. Palmer

Pennsylvania State University, University Park, PA

Paper No. SMASIS2016-9070, pp. V002T03A006; 10 pages
doi:10.1115/SMASIS2016-9070
From:
  • ASME 2016 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • Volume 2: Modeling, Simulation and Control; Bio-Inspired Smart Materials and Systems; Energy Harvesting
  • Stowe, Vermont, USA, September 28–30, 2016
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-5049-7
  • Copyright © 2016 by ASME

abstract

Nickel Titanium (NiTi) shape memory alloys (SMAs) exhibit shape memory and/or superelastic properties, enabling them to demonstrate multifunctionality by engineering microstructural and compositional gradients at selected locations. This paper focuses on the design optimization of NiTi compliant mechanisms resulting in single-piece structures with functionally graded properties, based on user-defined target shape matching approach. The compositionally graded zones within the structures will exhibit an on demand superelastic effect (SE) response, exploiting the tailored mechanical behavior of the structure. The functional grading has been approximated by allowing the geometry and the superelastic properties of each zone to vary. The superelastic phenomenon has been taken into consideration using a standard nonlinear SMA material model, focusing only on 2 regions of interest: the linear region of higher Young’s modulus of elasticity and the superelastic region with significantly lower Young’s modulus of elasticity. Due to an outside load, the graded zones reach the critical stress at different stages based on their composition, position and geometry, allowing the structure morphing. This concept has been used to optimize the structures’ geometry and mechanical properties to match a user-defined target shape structure. A multi-objective evolutionary algorithm (NSGA II - Non-dominated Sorting Genetic Algorithm) for constrained optimization of the structure’s mechanical properties and geometry has been developed and implemented.

Copyright © 2016 by ASME

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