The modelling of NiTi shape memory alloy functional properties by machine learning methods

Shape memory alloys (SMAs) exhibit several unique properties, including superelasticity and the shape memory effect. They can return to their original shape after deformation when heated. SMAs are widely used in various fields of science and technology. Shape memory alloys are functional materials that are used under loading, which in many cases is cyclic in nature. In the present study, the functional properties of NiTi shape memory alloys were modeled using supervised learning methods. The analysis was performed using Orange data mining software, which allows the creation of visual flowcharts and the generation of results in tables and graphs. The modeling was performed on four specimens. For each specimen, several functional properties, such as residual strain range De_r and dissipated energy range DW_dis. Each data set was divided into two unequal parts - the training and test sets. The training sets comprised 66% of the total data set. The remaining 34% was used for the test set. Among the methods studied, kNN, AdaBoost, Gradient Boosting and Random Forest showed the best results in terms of prediction errors. Therefore, ML learning methods are a powerful and promising tool for solving tasks related to the prediction of functional properties of SMAs.