A phenomenological constitutive model for one-dimensional shape memory alloys based on artificial neural network

Shape Memory Alloys (SMA) have become a material with great application prospects because of their unique characteristics and superior properties. A phenomenological constitutive model for SMA is constructed in the current paper. The proposed constitutive model is based on the phenomena observed in the experiment, and Artificial Neural Networks (ANN) are used to simulate part of the characteristics of SMA. The parameter identification method is also proposed, where Back-Propagation (BP) algorithm and the nonlinear optimisation algorithm are used at the same time. The numerical experiment has been carried out, which can well capture the constitutive relationship curve obtained from uniaxial tension and compression experiments of SMA, thus the model can be verified. The model can also describe the phase transformation characteristics of SMA well.