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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

REFERENCES

Zeng, Q.F. and Dong, G.N.: Superlubricity behaviors of Nitinol 60 alloy under oil lubrication. Trans. Nonferrous Met. Soc. China 24, 354 (2014).Google Scholar
Clingman, D.J., Calkins, F.T., Smith, J.P., and The Boeing Company: Thermo mechanical properties of Ni 60% weight Ti 40% weight. Smart Struct. Mater. 5053, 219 (2003).Google Scholar
Adharapurapu, R.R. and Vecchio, K.S.: Superelasticity in a new bioimplant material: Ni-rich 55NiTi alloy. Exp. Mech. 47, 365 (2007).CrossRefGoogle Scholar
Dehghani, K. and Khamei, A.A.: Hot deformation behavior of 60Nitinol (Ni 60wt%–Ti 40wt%) alloy: Experimental and computational studies. Mater. Sci. Eng., A 527, 684 (2010).Google Scholar
Khamei, A.A. and Dehghani, K.: A study on the mechanical behavior and microstructural evolution of Ni 60wt%–Ti4 0wt% (60Nitinol) intermetallic compound during hot deformation. Mater. Chem. Phys. 123, 269 (2010).Google Scholar
Khamei, A.A. and Dehghani, K.: Microstructural evolution during the hot deformation of Ti–55Ni (at. pct) intermetallic alloy. Metall. Mater. Trans. A 41A, 2595 (2010).Google Scholar
Shamsolhodaei, A., Zarei-Hanzaki, A., Ghambari, M., and Moemeni, S.: The high temperature flow behavior modeling of NiTi shape memory alloy employing phenomenological and physical based constitutive models: A comparative study. Intermetallics 53, 140 (2014).Google Scholar
Fatemi-Varzaneh, S.M., Zarei-Hanzaki, A., and Haghshenash, M.: A study on the effect of thermo-mechanical parameters on the deformation behavior of Mg–3Al–1Zn. Mater. Sci. Eng., A 497, 438 (2008).Google Scholar
Samantaray, D., Mandal, S., and Bhaduri, A.K.: Constitutive analysis to predict high-temperature flow stress in modified 9Cr-1Mo (P91) steel. Mater. Des. 31, 981 (2010).Google Scholar
McQueen, H.J. and Ryan, N.D.: Constitutive analysis in hot working. Mater. Sci. Eng., A 322, 43 (2002).Google Scholar
Fan, X.G. and Yang, H.: Internal-state-variable based self-consistent constitutive modeling for hot working of two-phase titanium alloys coupling microstructure evolution. Int. J. Plast. 27, 1833 (2011).Google Scholar
Slooff, F.A., Zhou, J., Duszczyk, J., and Katgerman, L.: Constitutive analysis of wrought magnesium alloy Mg-Al4-Zn1. Scr. Mater. 57, 759 (2007).Google Scholar
Rezaei Ashtiani, H.R., Parsa, M.H., and Bisadi, H.: Constitutive equations for elevated temperature flow behavior of commercial purity aluminum. Mater. Sci. Eng., A 545, 61 (2012).Google Scholar
Zeng, Z.P., Jonsson, S., and Zhang, Y.S.: Constitutive equations for pure titanium at elevated temperatures. Mater. Sci. Eng., A 505, 116 (2009).Google Scholar
Mandal, S., Rakesh, V., Sivaprasad, P.V., Venugopal, S., and Kasiviswanathan, K.V.: Constitutive equations to predict high temperature flow stress in a Ti-modified austenitic stainless steel. Mater. Sci. Eng., A 500, 114 (2009).Google Scholar
Peng, X.N., Guo, H.Z., Shi, Z.F., Qin, C., and Zhao, Z.L.: Constitutive equations for high temperature flow stress of TC4-DT alloy incorporating strain, strain rate and temperature. Mater. Des. 50, 198 (2013).Google Scholar
Guan, Z.P., Ren, M.W., Zhao, P., Ma, P.K., and Wang, Q.L.: Constitutive equations with varying parameters for superplastic flow behavior of Al-Zn-Mg-Zr alloy. Mater. Des. 54, 906 (2014).Google Scholar
Quan, G.Z., Liu, K.W., Zhou, J., and Chen, B.: Dynamic softening behaviors of 7075 aluminum alloy. Trans. Nonferrous Met. Soc. China 19, s537 (2009).Google Scholar
Zhao, J.W., Ding, H., Zhao, W.J., Huang, M.L., Wei, D.B., and Jiang, Z.Y.: Modelling of the hot deformation behaviour of a titanium alloy using constitutive equations and artificial neural network. Comput. Mater. Sci. 92, 47 (2014).Google Scholar
Han, Y., Qiao, G.J., Sun, J.P., and Zou, D.N.: A comparative study on constitutive relationship of as-cast 904L austenitic stainless steel during hot deformation based on Arrhenius-type and artificial neural network models. Comput. Mater. Sci. 67, 93 (2013).Google Scholar
Sabokpa, O., Zarei-Hanzaki, A., Abedi, H.R., and Haghdadi, N.: Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy. Mater. Des. 39, 390 (2012).Google Scholar
Kapoora, R., Palb, D., and Chakravarttya, J.K.: Use of artificial neural networks to predict the deformation behavior of Zr-2.5 Nb-0.5 Cu. J. Mater. Process. Technol. 169, 199 (2005).CrossRefGoogle Scholar
Otsuka, K. and Ren, X.: Physical metallurgy of Ti-Ni-based shape memory alloys. Prog. Mater. Sci. 50, 511 (2005).Google Scholar
Sellars, C.M. and McTegart, W.J.: On the mechanism of hot deformation. Acta Met. 14, 1136 (1966).CrossRefGoogle Scholar
Khamei, A.A. and Dehghani, K.: Modeling the hot-deformation behavior of Ni60wt%-Ti40wt% intermetallic alloy. J. Alloys Compd. 490, 377 (2010).Google Scholar
Xiao, X., Liu, G.Q., Hu, B.F., Zheng, X., Wang, L.N., Chen, S.J., and Ullah, A.: A comparative study on Arrhenius-type constitutive equations and artificial neural network model to predict high-temperature deformation behavior in 12Cr3WV steel. Comput. Mater. Sci. 62, 227 (2012).Google Scholar