PSI - Issue 28

Oleh Yasniy et al. / Procedia Structural Integrity 28 (2020) 1392–1398 Oleh Yasniy et al. / Structural Integrity Procedia 00 (2019) 000–000

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trees, support-vector machines, and k-nearest neighbors is also high. Therefore, the proposed methods of machine learning are powerful tools to evaluate the stress–strain diagrams of AMg6 aluminum alloy. References Strizhalo, V.A., Vorob’ev, E.V., 1997. Simulation of low-temperature discontinuous yield by the method of additional pulse loading. Strength of Material 29, 269  274. Strizhalo, V.A., Vorob’ev, E.V., 1993. Low-temperature interrupted yield of structural alloys. Strength of Material 25, 576  583. Strizhalo, V.A., Vorob’ev, E.V., 1999. Standardization of the strength of metals under conditions of low-temperature instability of plastic deformation and the action of strong magnetic fields. Strength of material 31, 459  466. Fedak, S., 2003. Jumplike deformation of the AMg6 alloy during creep. Visn. Ternopil. Derzh. Tekhn. Univ. 8, 16–23. [in Ukrainian] Yasnii, P. V., Fedak, S. I., Glad’o, V. B., Galushchak, M. P., 2004. Jumplike deformation in AMg6 aluminum alloy in tension. Strength of Materials 36, 113–118. Hastie, T., Tibshirani R., Friedman J., 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. In: New York, Springer, pp. 764. Yasnii, О. P., Pastukh, O. А., Pyndus, Yu. І., Lutsyk, N. S., Didych, I. S., 2018. Prediction of the Diagrams of Fatigue Fracture of D16T Aluminum Alloy by the Methods of Machine Learning. Materials Science 54, 333–338. Strizhalo, V.A., Vorob’ev, E.V., 1994. Low-temperature intermittent yield of hardenable materials. Strength of Material 26, 713  714. Bernshtein, M. L., Zaimovskii, V. A., 1970. Structure and Mechanical Properties of Metals. Metallurgiya, Moscow [in Russian]. Yasnii, P. V., Glad’o, V. B., 2002. The influence of the cyclic tensile loading component on the dislocation structure in AMg6 alloy. Fiz.-Khim. Mekh. Mater. 3, 63–68. Pidaparti, R. M. V., Palakal, M. J., 1995. Neural network approach to fatigue-crack-growth predictions under aircraft spectrum loadings. Journal of Aircraft 32, 825-831. Mohanty, J. R., Verma, B. B., Parhi, D. R. K., Ray D. R., 2009. Application of artificial neural network for predicting fatigue crack propagation

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