Hysteresis Model for Superelasticity of Shape Memory Alloy Based on ANN


Article Preview

Superelasticity is one of the most important properties of shape memory alloy. In this paper, the superelastic deformation behavior of NiTi shape memory alloy subjected to cyclic loading with stable superelasticity is experimentally investigated. According to test data, a constitutive model for the superelasticity of shape memory alloy is presented based on the artificial neural network (ANN). Numerical results agree well with experimental observations that verified the constitutive model being of high accuracy. This model can avoid the difficulties of other models on the determination of the parameters and is suitable for practical engineering application. Thus, a new method is provided for building the constitutive model of shape memory alloy.



Key Engineering Materials (Volumes 340-341)

Edited by:

N. Ohno and T. Uehara




H. N. Li et al., "Hysteresis Model for Superelasticity of Shape Memory Alloy Based on ANN", Key Engineering Materials, Vols. 340-341, pp. 1175-1180, 2007

Online since:

June 2007




[1] D. Cui, H. N. Li and G. B. Song: Progress on study and application of shape memory alloy in civil engineering, Journal of disaster prevention and mitigation engineering, Vol. 25(2005), No. 1, pp.86-94.

[2] J. Wang, Y. P. Shen and S. L. Wang: The development of the constitutive relation of a shape memory alloy, Shanghai journal of mechanics, Vol. 19(1998), No. 3, pp.185-195.

[3] K. Tanaka: A thermomechanical sketch of shape memory effect: one dimensional tensile behavior, Res. Mech, Vol. 18(1986), pp.251-263.

[4] C. Liang and C. A. Rogers: One-dimensional thermomechanical constitutive relations for shape memory materials, Journal of Intelligent Material Systems and Structure, No. 1(1990), pp.207-234.

DOI: 10.1177/1045389x9000100205

[5] L. C. Brinson: One dimensional constitutive behavior of shape memory alloys: thermomechanical derivation with non-constant material functions, Journal of Intelligent Material Systems and Structure, No. 4(1993), pp.229-242.

DOI: 10.1177/1045389x9300400213

[6] S. H. Liu, Y. L. Du and Z. Q. Jiang: The expression shape memory alloy stress-strain relationship, Journal of shijiazhuang railway institute, Vol. 14(2001), No. 4, pp.37-40.

[7] B. Wu, K. X. Sun, H. Li and W. S. Tang: Experimental research on mechanical properties of shape memory alloy, Earthquake engineering and engineering vibration, Vol. 19(1999), No. 2, pp.104-111.

[8] H. N. Robert: Theory of the backpropagation neural network, Neural Networks, 1989. IJCNN, Washington, DC, USA, Vol. 1(1989), pp.593-605.

Fetching data from Crossref.
This may take some time to load.