PSI - Issue 36

Iryna Didych et al. / Procedia Structural Integrity 36 (2022) 166–170 Iryna Didych et al. / Structural Integrity Procedia 00 (2021) 000 – 000

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jump-like creep obtained by the method of neural networks.

obtained by the method of neural networks.

The dependences between the corresponding tensile stress and the value of jump-like increments of creep log10(  )  log10(  p) are shown in Fig. 4. It was determined that the error of neural networks method was equal to 4.8%. The obtained results well agree with the experimental ones. 5. Conclusions The predicted jump-like creep by method of neural networks is in good agreement with the experimental data. In addition, neural networks training should be carried out according to the algorithm of supervised learning to ensure the correct network operation. It was found that the neural networks prediction accuracy is 96.2%. Therefore, the proposed method of machine learning is powerful tool that can be used to solve problems of mechanics. References Fedak, S., 2003. Jumplike deformation of the AMg6 alloy during creep. Visn. Ternopil. Derzh. Tekhn. Univ. 8, 16 – 23. [in Ukrainian] Goodfellow, I., Bengio, Y., Courville, A., 2016. Deep Learning, The MIT Press, pp. 800. Gurney, K., 1997. An introduction to neural networks, First Ed., Taylor & Francis Group, London, pp. 317. Hastie, T., Tibshirani R., Friedman J., 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. In: New York, Springer, pp. 764. Haykin, S., 1999. Neural Networks: A Comprehensive Foundation. Second Ed., Prentice Hall, Canada, pp. 823. Kang, J. Y., Choi, B. I., Lee, H. J., 2006. Application of artificial neural network for predicting plain strain fracture toughness using tensile test results. Fatigue Fract. Eng. Mater. Struct. 29, 321 – 329. Konovalenko, I.V., Maruschak, P.O., 2021. Classification of the surface technological defects in rolled metal products with the help of a deep neural network. Materials Science. DOI 10.1007/s11003-021-00495-5 Mohanty, J. R., Verma, B. B., Parhi, D. R. K., Ray D. R., 2009. Application of artificial neural network for predicting fatigue crack propagation life of aluminum alloys. Archives of Computational Materials Science and Surface Engineering 1, 133 – 138. 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. Richard, D. N., 1998. Applied regression analysis, Third Ed., John Wiley & Sons, New York, pp. 736. Smola, A., Vishwanathan, S.V.N., 2010. Introduction to Machine Learning , Cambridge University Press, pp. 234. Strizhalo, V.A., Vorob’ev, E.V., 1993. Low-temperature interrupted yield of structural alloys. Strength of Materials 25, 576  583. 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., 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 Materials 31, 459  466. Yasnii, P., Halushchak, M., Stoyanova, O., Fedak, S., 2001. Microstructural Features of deformation of AMg6 alloy under conditions of creep and tension. Materials Science 37, 762-768. Yasnii, P.V., Hlad'o, V.B., 2002. Effect of the cyclic tensile component of loading on the dislocation structure of AMg6 alloy. Materials Science 38(3), 388 – 393. Yasnii, О. P., Pastukh, O. А., Pyndus, Yu. І., Lutsyk, N. S., Didych, I. S., 2018. Prediction of the diagrams of fatigu e fracture of D16T aluminum alloy by the methods of machine learning. Materials Science 54, 333 – 338. Yasniy, O., Didych, I., Fedak, S., Lapusta, Yu., 2020. Modeling of AMg6 aluminum alloy jump-like deformation properties by machine learning methods. Procedia Structural Integrity 28, 1392 – 1398. 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, P., Galushchak, M., 1998. Methodology and some results of research of influence of cyclic loading on diagrams of deformation of AMg6 alloy. Visn. Ternopil. Derzh. Tekhn. Univ. 3, 62 – 66.