EVALUATION OF STRUCTURAL ELEMENTS LIFETIME BY NEURAL NETWORK

Authors

  • Iryna Didych
  • Oleh Pastukh
  • Yuri Pyndus
  • Oleh Yasniy

Keywords:

fatigue crack growth, stress intensity factor, neural network, lifetime, data science

Abstract

While in operation structural elements may have cracks, which usually grow up to critical size due to cyclic loading and the component is likely to be destroyed. Therefore, it is important to study the process of fatigue failure of structural materials. The aim of this study was to evaluate the lifetime of structural elements, taking into account the achieved level of material damage, and to predict the fatigue crack growth (FCG) rate in an aluminium D16chT alloy under regular loading by neural network (NN). The results obtained by the authors are in good agreement with the experimental data.

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Published

2018-04-30

How to Cite

Didych, I. ., Pastukh, O. ., Pyndus, Y. ., & Yasniy, O. . (2018). EVALUATION OF STRUCTURAL ELEMENTS LIFETIME BY NEURAL NETWORK. Acta Metallurgica Slovaca, 24(1), 82–87. Retrieved from https://journals.scicell.org/index.php/AMS/article/view/250

Issue

Vol. 24 No. 1 (2018): Volume 24, no. 1, 2018

Section

Research papers