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Rapid Characterization of Shape Memory Alloy Material Parameters Using Computational Intelligence Methods

[+] Author Affiliations
James V. Henrickson, Kenton Kirkpatrick, John Valasek

Texas A&M University, College Station, TX

Paper No. SMASIS2013-3016, pp. V001T01A001; 10 pages
doi:10.1115/SMASIS2013-3016
From:
  • ASME 2013 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • Volume 1: Development and Characterization of Multifunctional Materials; Modeling, Simulation and Control of Adaptive Systems; Integrated System Design and Implementation
  • Snowbird, Utah, USA, September 16–18, 2013
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-5603-1
  • Copyright © 2013 by ASME

abstract

Shape memory alloys are capable of delivering advantageous solutions to a wide range of engineering-based problems. Implementation of these solutions, however, is often complicated by the hysteretic, non-linear, thermo-mechanical behavior of the material. Although existing shape memory alloy constitutive models are largely accurate in describing this unique behavior, they require prior characterization of the material parameters. Consequently, before thorough modeling and simulation can occur for a shape memory alloy-based project, one must first go through the process of identifying several material parameters unique to shape memory alloys. Current characterization procedures necessitate extensive experimentation, data collection, and data processing. As a result, these methods simultaneously create a high barrier of entry for engineers new to active materials and impede the advanced study of shape memory alloy material parameter evolution. This paper develops a novel method in which computational intelligence methods are used to rapidly identify shape memory alloy material parameters. Specifically, an artificial neural network is trained to identify transformation temperatures and stress influence coefficients of given shape memory alloy specimens using strain-temperature coordinates as inputs. After generating training data through the use of a constitutive model, the resulting trained artificial neural network was used to identify parameters for a number of randomly generated theoretical shape memory alloys. Results presented in the paper show that the artificial neural network was able to rapidly identify both transformation temperatures and stress influence coefficients with satisfactory accuracy. The generation of training data was then repeated using Taguchi methods. Further results presented in the paper show that the artificial neural network trained with the Taguchi-based training data yielded improved characterization accuracy while using less training data.

Copyright © 2013 by ASME

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