Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-28T06:21:17.287Z Has data issue: false hasContentIssue false
  • English
  • Français

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: niatlusq@126.com
Get access

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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