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Materials Science

, Volume 54, Issue 3, pp 333–338 | Cite as

Prediction of the Diagrams of Fatigue Fracture of D16T Aluminum Alloy by the Methods of Machine Learning

  • О. P. Yasnii
  • O. А. Pastukh
  • Yu. І. Pyndus
  • N. S. Lutsyk
  • I. S. Didych
Article
  • 6 Downloads

By the methods of machine learning (neural networks, boosted trees, random forests, support-vector machines, and k -nearest neighbors), we plotted the diagrams of fatigue fracture of D16T aluminum alloy under regular loading with a stress ratio R = 0, 0.2, 0.4, and 0.6. The obtained results are in good agreement with the experimental data. It was discovered that the method of neural networks gives the least prediction error equal to 3.2 and 2.5% in tested samples.

Keywords

fatigue crack growth stress intensity factor load ratio durability neural network machine learning 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • О. P. Yasnii
    • 1
  • O. А. Pastukh
    • 1
  • Yu. І. Pyndus
    • 1
  • N. S. Lutsyk
    • 1
  • I. S. Didych
    • 1
  1. 1.Pulyui Ternopil’ National Technical UniversityTernopil’Ukraine

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