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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

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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.

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References

  1. Q.F. Zeng and G.N. Dong: Superlubricity behaviors of Nitinol 60 alloy under oil lubrication. Trans. Nonferrous Met. Soc. China 24, 354 (2014).

    Article  CAS  Google Scholar 

  2. D.J. Clingman, F.T. Calkins, J.P. Smith, and The Boeing Company: Thermo mechanical properties of Ni 60% weight Ti 40% weight. Smart Struct. Mater. 5053, 219 (2003).

    CAS  Google Scholar 

  3. R.R. Adharapurapu and K.S. Vecchio: Superelasticity in a new bioimplant material: Ni-rich 55NiTi alloy. Exp. Mech. 47, 365 (2007).

    Article  CAS  Google Scholar 

  4. K. Dehghani and A.A. Khamei: Hot deformation behavior of 60Nitinol (Ni 60wt%–Ti 40wt%) alloy: Experimental and computational studies. Mater. Sci. Eng., A 527, 684 (2010).

    Article  Google Scholar 

  5. A.A. Khamei and K. Dehghani: 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).

    Article  CAS  Google Scholar 

  6. A.A. Khamei and K. Dehghani: Microstructural evolution during the hot deformation of Ti–55Ni (at. pct) intermetallic alloy. Metall. Mater. Trans. A 41A, 2595 (2010).

    Article  CAS  Google Scholar 

  7. A. Shamsolhodaei, A. Zarei-Hanzaki, M. Ghambari, and S. Moemeni: 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).

    Article  CAS  Google Scholar 

  8. S.M. Fatemi-Varzaneh, A. Zarei-Hanzaki, and M. Haghshenash: A study on the effect of thermo-mechanical parameters on the deformation behavior of Mg–3Al–1Zn. Mater. Sci. Eng., A 497, 438 (2008).

    Article  Google Scholar 

  9. D. Samantaray, S. Mandal, and A.K. Bhaduri: Constitutive analysis to predict high-temperature flow stress in modified 9Cr-1Mo (P91) steel. Mater. Des. 31, 981 (2010).

    Article  CAS  Google Scholar 

  10. H.J. McQueen and N.D. Ryan: Constitutive analysis in hot working. Mater. Sci. Eng., A 322, 43 (2002).

    Article  Google Scholar 

  11. X.G. Fan and H. Yang: 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).

    Article  CAS  Google Scholar 

  12. F.A. Slooff, J. Zhou, J. Duszczyk, and L. Katgerman: Constitutive analysis of wrought magnesium alloy Mg-Al4-Zn1. Scr. Mater. 57, 759 (2007).

    Article  CAS  Google Scholar 

  13. H.R. Rezaei Ashtiani, M.H. Parsa, and H. Bisadi: Constitutive equations for elevated temperature flow behavior of commercial purity aluminum. Mater. Sci. Eng., A 545, 61 (2012).

    Article  CAS  Google Scholar 

  14. Z.P. Zeng, S. Jonsson, and Y.S. Zhang: Constitutive equations for pure titanium at elevated temperatures. Mater. Sci. Eng., A 505, 116 (2009).

    Article  Google Scholar 

  15. S. Mandal, V. Rakesh, P.V. Sivaprasad, S. Venugopal, and K.V. Kasiviswanathan: Constitutive equations to predict high temperature flow stress in a Ti-modified austenitic stainless steel. Mater. Sci. Eng., A 500, 114 (2009).

    Article  Google Scholar 

  16. X.N. Peng, H.Z. Guo, Z.F. Shi, C. Qin, and Z.L. Zhao: Constitutive equations for high temperature flow stress of TC4-DT alloy incorporating strain, strain rate and temperature. Mater. Des. 50, 198 (2013).

    Article  CAS  Google Scholar 

  17. Z.P. Guan, M.W. Ren, P. Zhao, P.K. Ma, and Q.L. Wang: Constitutive equations with varying parameters for superplastic flow behavior of Al-Zn-Mg-Zr alloy. Mater. Des. 54, 906 (2014).

    Article  CAS  Google Scholar 

  18. G.Z. Quan, K.W. Liu, J. Zhou, and B. Chen: Dynamic softening behaviors of 7075 aluminum alloy. Trans. Nonferrous Met. Soc. China 19, s537 (2009).

    Article  CAS  Google Scholar 

  19. J.W. Zhao, H. Ding, W.J. Zhao, M.L. Huang, D.B. Wei, and Z.Y. Jiang: Modelling of the hot deformation behaviour of a titanium alloy using constitutive equations and artificial neural network. Comput. Mater. Sci. 92, 47 (2014).

    Article  CAS  Google Scholar 

  20. Y. Han, G.J. Qiao, J.P. Sun, and D.N. Zou: 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).

    Article  CAS  Google Scholar 

  21. O. Sabokpa, A. Zarei-Hanzaki, H.R. Abedi, and N. Haghdadi: Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy. Mater. Des. 39, 390 (2012).

    Article  CAS  Google Scholar 

  22. R. Kapoora, D. Palb, and J.K. Chakravarttya: 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).

    Article  Google Scholar 

  23. K. Otsuka and X. Ren: Physical metallurgy of Ti-Ni-based shape memory alloys. Prog. Mater. Sci. 50, 511 (2005).

    Article  CAS  Google Scholar 

  24. C.M. Sellars and W.J. McTegart: On the mechanism of hot deformation. Acta Met. 14, 1136 (1966).

    Article  CAS  Google Scholar 

  25. A.A. Khamei and K. Dehghani: Modeling the hot-deformation behavior of Ni60wt%-Ti40wt% intermetallic alloy. J. Alloys Compd. 490, 377 (2010).

    Article  CAS  Google Scholar 

  26. X. Xiao, G.Q. Liu, B.F. Hu, X. Zheng, L.N. Wang, S.J. Chen, and A. Ullah: 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).

    Article  CAS  Google Scholar 

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ACKNOWLEDGMENTS

We would like to thank our anonymous reviewers for extremely helpful comments. We acknowledge the support from the National Natural Science Foundation of China (Grant No. 51164030) and Natural Science Foundation of Jiangxi Province (Grant No. 20122 BAB216018).

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Authors and Affiliations

  1. Institute of Material Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu, 211106, China

    Xiaoyong Shu

  2. School of Material Science and Engineering, Nanchang Hangkong University, Jiangxi, 330063, China

    Xiaoyong Shu, Shiqiang Lu, Kelu Wang & Guifa Li

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  1. Xiaoyong Shu
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  2. Shiqiang Lu
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  4. Guifa Li
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Correspondence to Shiqiang Lu.

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Shu, X., Lu, S., Wang, K. et al. A comparative study on constitutive equations and artificial neural network model to predict high-temperature deformation behavior in Nitinol 60 shape memory alloy. Journal of Materials Research 30, 1988–1998 (2015). https://doi.org/10.1557/jmr.2015.144

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  • DOI: https://doi.org/10.1557/jmr.2015.144

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