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A comparative study on constitutive equations and artificial neural network model to predict high-temperature deformation behavior in Nitinol 60 shape memory alloy

Published online by Cambridge University Press:  27 May 2015

Xiaoyong Shu ,
Shiqiang Lu ,
Kelu Wang  and
Guifa Li
Xiaoyong Shu
Affiliation:
Institute of Material Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu 211106, China; and School of Material Science and Engineering, Nanchang Hangkong University, Jiangxi 330063, China
Shiqiang Lu*
Affiliation:
School of Material Science and Engineering, Nanchang Hangkong University, Jiangxi 330063, China
Kelu Wang
Affiliation:
School of Material Science and Engineering, Nanchang Hangkong University, Jiangxi 330063, China
Guifa Li
Affiliation:
School of Material Science and Engineering, Nanchang Hangkong University, Jiangxi 330063, China
*
a)Address all correspondence to this author. e-mail: [email protected]
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Abstract

The present study was conducted to predict the hot deformation behavior of the as-forged Nitinol 60 shape memory alloy by using the Arrhenius type, multiple-linear, and artificial neural network (ANN) models. The acquired flow stress data from isothermal hot compression tests in a temperature range of 650–850 °C under strain rate range of 0.01–1 s−1 were used to calculate the material constants for establishing the corresponding constitutive equations. Furthermore, a comparative study has been made on the capability of the aforementioned models to predict the high-temperature deformation behavior by comparing the prediction relative errors, average absolute relative error, and correlation coefficient. The results show that multiple-linear model predicts the flow behavior more accurately than the Arrhenius type model. The ANN model is much more efficient and has a better prediction power for the as-forged Nitinol 60 alloy than both the Arrhenius type and multiple-linear models.

Keywords

Nitinol 60 shape memory alloyhot deformation behaviorconstitutive equationartificial neural network
Type
Articles
Information
Journal of Materials Research , Volume 30 , Issue 12 , 28 June 2015 , pp. 1988 - 1998
Copyright
Copyright © Materials Research Society 2015 

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References

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