This paper investigates the use of machine learning methods to predict the loading frequency of shape memory alloys (SMAs) based on experimental data. SMAs, in particular nickel-titanium (NiTi) alloys, have unique properties that restore the original shape after significant deformation. The frequency of loading significantly affects the functional characteristics of SMAs. Experimental data were obtained from cyclic tensile tests of a 1.5 mm diameter Ni_55.8Ti_44.2 wire at different loading frequencies (0.1, 0.5, 1.0, and 5.0 Hz). Various machine learning methods were used to predict the loading frequency f (Hz) based on input parameters such as stress σ (MPa), number of cycles N, strain ε (%), and loading–unloading stage: boosted trees, random forest, support vector machines, k-nearest neighbors, and artificial neural networks of the MLP type. Experimental data of 100–140 load–unload cycles for four load frequencies were used for training. The dataset contained 13,365 elements. The results showed that the MLP neural network model demonstrated the highest accuracy in load frequency classification. The boosted trees and random forest models also performed well, although slightly below MLP. The SVM method also performed quite well. The KNN method showed the worst results among all models. Additional testing of the MLP model on cycles that were not included in the training data (200th, 300th, and 1035th cycles) showed that the model retains high efficiency in predicting load frequency, although the accuracy gradually decreases on later cycles due to the accumulation of structural changes in the material.

The influence of the cyclic loading on the functional properties of NiTi was studied. Cylindrical specimens with a diameter of 4 mm and a gage length of 12.5 mm were tested under uniaxial cyclic loading with control crosshead displacement at a temperature of 0°C. The dependences of the stress and strain range as well as dissipation energy on the number of loading cycles at different initial stress range were analysed. During the first 10 loading cycles, a rapid decrease in the strain range and energy dissipation was observed. Dissipation energy was invariant to the loading cycles’ number at N > 20 cycles and to the stress range that did not exceed the martensite finish stress level, was within the same scatter band and can be described by the single dependence. With the stress range growth at N < 20 cycles from 509 to 740 MPa, the value of dissipation energy increases and that of relative dissipation energy decreases. Loss coefficient, which characterises material damping ability, significantly decreases during the first 10 loading cycles and remains practically unchanged up to the failure of the specimens. At the stabilisation area, the loss coefficient is almost non-sensitive towards the stress range.