Oleh Yasniy

The study investigates the possibility of predicting the hysteresis behaviour of a NiTi shape memory alloy (SMA) under cyclic loading using machine learning methods. Based on experimental test data obtained from SMA wire specimens, an ensemble model of the Stacking Regressor type is developed, combining the Random Forest, Gradient Boosting, Extremely Randomised Trees, k-Nearest Neighbours, Support Vector Regression, and Multi-Layer Perceptron algorithms. The ElasticNet algorithm is employed as the meta-model. The ensemble structure and model hyperparameters are optimised using the GridSearchCV procedure with group cross-validation. The results demonstrate high accuracy in strain prediction across different cyclic loading frequencies. The coefficient of determination R² for the test data exceeds 0.996. The proposed model also exhibits strong generalisation when predicting material behaviour beyond the training range and accurately reproduces the shape of hysteresis loops in the stress-strain diagram.

Fatigue crack growth rate is modelled by neural network and compared with analytical models, namely, the Paris' law and polynomial log regressions of the second to fifth degree. To assess the accuracy of the modelling, the determination coefficient R², mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to assess the accuracy of the modelling. Results show that both approaches, in particular, the classical analytical models and the neural network, provide the high accuracy of approximation of the experimental data. At the same time, the neural network demonstrates an advantage in the region of high values of DK, where a nonlinear acceleration of crack growth is observed, reaching a coefficient of determination R² equal to 0.9994. The presented approach confirms the effectiveness of applying machine learning methods, in particular neural networks, in the problems of fracture mechanics and fatigue analysis.

This study presents an approach for predicting the hysteresis behavior of shape memory alloys (SMAs) based on automated machine learning (AutoML) integrated with explainable artificial intelligence (XAI). Experimental data from cyclic tests of NiTi wire under loading frequencies of 0.3, 0.5, 1, and 5 Hz were used for model development. The AutoML framework PyCaret enabled automated model selection, hyperparameter optimization, and performance comparison of regression algorithms. The highest prediction accuracy was achieved by the LightGBM model (for 0.3 Hz and 1 Hz) and the CatBoost model (for 0.5 Hz and 5 Hz), both demonstrating a coefficient of determination R² > 0.997 and low MSE, MAE, and MAPE values. Integration of XAI through the SHAP method provided both global and local interpretability of the model’s behavior. The analysis revealed the dominant influence of the Stress parameter, the physically meaningful role of the loading or unloading stage (UpDown), and a gradual increase in the contribution of the Cycle parameter in later cycles, reflecting fatigue accumulation effects. The obtained results confirm the high accuracy, interpretability, and physical consistency of the developed models.

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 Der and dissipated energy range DWdis. 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.

The paper presents a comparative analysis of various machine learning algorithms for the classification of epoxy composites reinforced with basalt fiber and modified with inorganic fillers. The classification is based on key thermophysical characteristics, in particular, the mass fraction of the filler, temperature, and thermal conductivity coefficient. A dataset of 16,056 interpolated samples was used to train and evaluate more than a dozen models. Among the tested algorithms, the MLP neural network model showed the highest accuracy of 99.7% and balanced classification metrics F1-measure and G-Mean. Ensemble methods, including XGBoost, CatBoost, ExtraTrees, and HistGradientBoosting, also showed high classification accuracy. To interpret the results of the MLP model, SHAP analysis was applied, which confirmed the predominant influence of the mass fraction of the filler on decision-making for all classes. The results of the study confirm the high effectiveness of machine learning methods for recognizing filler type in composite materials, as well as the potential of interpretable AI in materials science tasks.

Shape memory alloys (SMAs) have found widespread application in various fields of science and technology due to their unique properties, such as superelasticity and shape memory effect. These alloys retain their initial form by memorising it between two transformation phases, which is temperature or magnetic field-dependent. The application of such materials is straightforward. The alloy can be deformed by force and recover to its initial shape or size after heating over a specific temperature. There are a lot of various kinds of SMA, for instance, Fe–Mn–Si, Cu–Zn–Al, and Cu–Al–N, and every type of SMA is applied specifically, though Nitinol Ni-Ti is ubiquitous because of its stable properties SMAs are widely used in medicine, the aerospace industry, motor building, civil engineering, dentistry, etc. During their operation, structural elements made of SMAs undergo long-term cyclic loading that can lead to premature loss of functional properties, exhaustion of lifetime, and subsequent failure. Therefore, ensuring sufficient functional properties and endurance of SMA is necessary. Often, the experiments are quite costly and time-consuming and require expert knowledge. Therefore, it is crucial to model the functional and structural properties of SMAs by employing AI (Artificial intelligence) and machine learning (ML) methods. AI can be employed to model SMA behaviour. AI is actively used in material science and fracture mechanics ML is a part of AI that can efficiently solve complicated tasks. This study aims to perform a comprehensive review of the application of ML methods to estimate various properties of shape memory alloys. A comprehensive analysis of ML methods was performed as applied to modelling various properties of SMAs. Several studies concern the application of methods of AI and ML to solve such problems. In general, AI and ML methods are promising and powerful tools to model the SMAs properties. Nevertheless, there is always room for improvement and further elaboration of the aforementioned methods and approaches for modelling the functional and structural properties of SMAs

Shape memory alloys (SMAs) are a class of materials that have the ability to return to their previous shape when exposed to temperature or mechanical stress. The main functional properties of these alloys, the shape memory effect (SME) and superelasticity (SE), make them indispensable in various industries. The SMA superelasticity is the ability of a material to return to its original shape after loading and unloading due to transformations between austenite and martensite. These phase transitions are accompanied by hysteresis, which can be observed in the stress-strain diagram. In this study, the hysteresis behavior of SMA, particularly nickel-titanium alloy (NiTi or Nitinol), was modeled using artificial neural networks. The use of neural networks in the study made it possible to obtain accurate material strain predictions and reduce the number of actual experiments. The results showed the high accuracy of the prediction model, which indicates the prospects of using artificial neural networks in the study of SMA characteristics.

In the current study, the fatigue life of QSTE340TM steel was modelled using a machine learning method, namely, a neural network. This problem was solved by a Multi-Layer Perceptron (MLP) neural network with a 3-75-1 architecture, which allows the prediction of the crack length based on the number of load cycles N, the stress ratio R, and the overload ratio Rol. The proposed model showed high accuracy, with mean absolute percentage error (MAPE) ranging from 0.02% to 4.59% for different R and Rol. The neural network effectively reveals the nonlinear relationships between input parameters and fatigue crack growth, providing reliable predictions for different loading conditions.

The paper presents the results of studies of the physical and mechanical properties of materials: compressive failure stress, tensile failure stress, impact strength, and residual stresses in the material. To determine these parameters, we used modern methods of analyzing experimental studies, neural networks, to ensure a high measurement accuracy. Particular attention is paid to the research of the impact strength of residual stresses in the material, which allows for predicting the behavior of products in service. Impact toughness is one of the main indicators for evaluating materials that are operated under complex stress–strain conditions. It is proved that the values of both the compressive and tensile destructive stresses, impact strength, and residual stresses significantly affect the ability of the material to resist fracture under the action of both static and dynamic alternating loads. Critical values of residual stresses have been determined, the excess of which reduces the resistance of materials to fracture. The research has established the regularities of residual stresses and their magnitude in materials with different structural organizations. The research results are important from a scientific and technical point of view for the creation of new composite materials with high resistance to fracture, which will increase the operational reliability of the product as a whole.