PSI - Issue 72

Oleh Yasniy et al. / Procedia Structural Integrity 72 (2025) 188–194

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prediction errors, such as MAE, MSE, RMSE, and MAPE, confirm the high accuracy of the model during both the loading and unloading stages. Importantly, the model confirmed its ability to accurately predict the material behavior for a cycle it had not seen during training. This indicates the model's ability to generalize and predict material behavior under different loading conditions. Thus, using MLP neural networks to predict the hysteresis behavior of shape memory alloys has proven effective. This allowed us to significantly reduce the need for experimental studies and provide accurate predictions of material behavior at different loading frequencies. References Bettini, P., Rigamonti, D., Sala, G., 2021. Chapter 16 - SMA for composite aerospace structures, Shape Memory Alloy Engineering (Second Edition), Butterworth-Heinemann, 561-590 Noruzi, A., Mohammadimehr, M., Bargozini, F., 2024. Experimental free vibration and tensile test results of a five-layer sandwich plate by comparing various carbon nanostructure reinforcements with SMA, Heliyon 10(10), e31164 Kim, T., See, C. W., Li, X., & Zhu, D., 2020. Orthopedic implants and devices for bone fractures and defects: Past, present and perspective, Engineered Regeneration 1, 2020, 6-18 Iasnii, V., Krechkovska, H., Budz, V., Student, O., & Lapusta, Y., 2024. Frequency effect on low-cycle fatigue behavior of pseudoelastic NiTi alloy. Fatigue and Fracture of Engineering Materials and Structures 47 (8), 2857 – 2872 Yasniy, O., Pasternak, I., Didych, I., Fedak, S., Tymoshchuk, D., 2023. Methods of jump-like creep modeling of AMg6 aluminum alloy. Procedia Structural Integrity 48, 149-154. Maruschak, P., Konovalenko, I., Osadtsa, Y., Medvid, V., Shovkun, O., Baran, D., Kozbur, H., Mykhailyshyn, R., 2024. Surface Illumination as a Factor Influencing the Efficacy of Defect Recognition on a Rolled Metal Surface Using a Deep Neural Network. Applied Sciences. 14(6):2591. Tymoshchuk, D., Yasniy, O., Maruschak, P., Iasnii, V., Didych, I., 2024. Loading Frequency Classification in Shape Memory Alloys: A Machine Learning Approach. Computers, 13(12), 339. Klots, Y., Petliak, N., Martsenko, S., Tymoshchuk, V., Bondarenko, I., 2024. Machine Learning system for detecting malicious traffic generated by IoT devices. CEUR Workshop Proceedings, 3742, 97 - 110 Tymoshchuk, D., Yasniy, O., Mytnyk, M., Zagorodna, N., Tymoshchuk, V., 2024. Detection and classification of DDoS flooding attacks by machine learning methods. CEUR Workshop Proceedings, 3842, 184 - 195 Lyashuk, O., Stashkiv, M., Lytvynenko, I., Sakhno, V., Khoroshun, R., 2023. Information Technologies Use in the Study of Functional Properties of Wheeled Vehicles. CEUR Workshop Proceedings, 3628, 500 - 512 Yasniy, O., Didych, I., Lapusta, Yu., 2020. Prediction of Fatigue Crack Growth Diagrams by Methods of Machine Learning Under Constant Amplitude Loading. Acta Metallurgica Slovaca 26, 31 – 33. Yasniy, O., Tymoshchuk, D., Didych, I., Zagorodna, N., Malyshevska, O., 2024. Modelling of automotive steel fatigue lifetime by machine learning method. CEUR Workshop Proceedings, 3896, 165 - 172 Iasnii V., Bykiv N., Yasniy O., Budz V., 2022. Methodology and some results of studying the influence of frequency on functional properties of pseudoelastic SMA. Scientific Journal of TNTU (Tern.), 107(3): 45-50 Haykin, S., 2009. Neural networks and learning machines. 3rd ed, Prentice Hall. Hamilton, Ontario, 936.