PSI - Issue 72

Oleh Yasniy et al. / Procedia Structural Integrity 72 (2025) 181–187

187

Pratoori, R., 2023. Comparison of Random Forest and Neural Network Framework for Prediction of Fatigue Crack Growth Rate in Nickel Superalloys. arXiv preprint arXiv:2309.13534 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 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., Mytnyk, M., Maruschak, P., Mykytyshyn, A., & Didych, I., 2024. Machine learning methods as applied to modelling thermal conductivity of epoxy-based composites with different fillers for aircraft. Aviation, 28(2), 64-71 Yasnii, О. P., Pastukh, O. А., Pyndus, Yu. І., Lutsyk, N. S., Didych, I. S., 2018. Prediction of the Diagrams of Fatigue Frac ture of D16T Aluminum Alloy by the Methods of Machine Learning. Materials Science 54, 333 – 338 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 Zarrabi, K., Lu, W. W., Hellier, A. K., 2008. An Artificial Neural Network Approach to Fatigue Crack Growth. Journal of Mechanical Engineering Science 222, 1389 – 1395 Zhang, S., Chen, X., Liu, Y., 2021. Prediction of Fatigue – Crack Growth with Neural Network-Based Increment Learning Scheme. Engineering Fracture Mechanics 250, 107744