A Neural Network Model of Restrained Recovery for Shape Memory Alloys

Abstract:

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The methods of constitutive modeling of restrained recovery for Shape memory alloys (SMAs) were described in this paper and experiments were carried out to provide the essential data for the methods. The present mathematical constitutive models are inconvenient for engineering applications. Then a back propagation (BP) neural network model was developed for restrained recovery of SMAs. This BP neural network model can learn the hysteresis of SMAs in the process of heating and cooling based on its properties of nonlinear function mapping and adaptation, and it can predict the complete restrained recovery stress of SMAs with different initial strains. The predicted results obtained from the proposed BP model agree well with the experimental data. Moreover, the proposed BP model is more simple, convenient and low cost compared with the present mathematical constitutive models.

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Periodical:

Edited by:

Ying Zhang and Ping He

Pages:

172-177

DOI:

10.4028/www.scientific.net/AMR.650.172

Citation:

S. Wu et al., "A Neural Network Model of Restrained Recovery for Shape Memory Alloys", Advanced Materials Research, Vol. 650, pp. 172-177, 2013

Online since:

January 2013

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Price:

$35.00

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