Prediction of transformation temperatures of NiTiZr shape memory alloys using artificial neural network

Abstract

In this study, detailed analyses have been made to predict the transition temperatures (Ms, Mf, As and Af) and thermal hysteresis of ternary NiTiZr shape memory alloys (SMAs) using an artificial neural network (ANN). The ANN model was built, trained and tested using 35 data points pertaining to Ni-Ti-Zr collected from the literature spanning over 25 years. The alloys that were subjected to age hardening were excluded from the study. In this work, we have used 11 inputs, including the atomic percentage of the constituting elements, i.e. Ni, Ti, and Zr, number of valence electrons, atomic number, valence electron concentration, atomic radius, electronegativity, melting temperature, pseudopotential radius, and atomic mass. By determining the correlation coefficient (R) of different combinations of layers and neurons, an ideal neural network was built. Using these data points, the ANN model was trained and tested in order to produce the output parameters, namely the transition temperatures of the SMAs. The transformation temperatures as predicted by the ANN are compared with those for the predetermined target values. An analysis of the errors involved in predicting the transformation temperatures is also included. The transition temperatures of novel Ni-Ti-Zr alloy compositions were estimated, and their Ms temperatures were set to be substantially above 400 K. The experimentally produced alloy compositions were characterized for their characteristic temperatures of transition using a differential scanning calorimeter so as to validate the outputs obtained.