Target shape optimization of functionally graded shape memory alloy compliant mechanisms

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3 Citations (Scopus)

Abstract

This article focuses on the design optimization of shape memory alloy compliant mechanisms with functionally graded properties to achieve a user-defined target shape. The functional grading is approximated by allowing the geometry and the modulus of elasticity of each zone to vary. The superelastic phenomenon has been taken into account using a standard nonlinear shape memory alloy material model with linear region of higher modulus of elasticity and a superelastic region with much lower modulus of elasticity. A large deflection beam model is integrated with a multi-objective evolutionary algorithm for constrained optimization of the structure’s mechanical properties and geometry. Examples illustrate the trade-offs between the objectives of minimizing shape error, maximum stress, and volume. It is observed that in the optimized designs, the elastic modulus and the geometry work together in regions where large flexibility is required to achieve the target shape.

Original languageEnglish (US)
Pages (from-to)1385-1396
Number of pages12
JournalJournal of Intelligent Material Systems and Structures
Volume30
Issue number9
DOIs
StatePublished - May 1 2019

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Compliant mechanisms
Shape optimization
Shape memory effect
Elastic moduli
Geometry
Constrained optimization
Evolutionary algorithms
Mechanical properties

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Mechanical Engineering

Cite this

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abstract = "This article focuses on the design optimization of shape memory alloy compliant mechanisms with functionally graded properties to achieve a user-defined target shape. The functional grading is approximated by allowing the geometry and the modulus of elasticity of each zone to vary. The superelastic phenomenon has been taken into account using a standard nonlinear shape memory alloy material model with linear region of higher modulus of elasticity and a superelastic region with much lower modulus of elasticity. A large deflection beam model is integrated with a multi-objective evolutionary algorithm for constrained optimization of the structure’s mechanical properties and geometry. Examples illustrate the trade-offs between the objectives of minimizing shape error, maximum stress, and volume. It is observed that in the optimized designs, the elastic modulus and the geometry work together in regions where large flexibility is required to achieve the target shape.",
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