PREDICTION OF FATIGUE CRACK GROWTH DIAGRAMS BY METHODS OF MACHINE LEARNING UNDER CONSTANT AMPLITUDE LOADING

Authors

  • Oleh Yasniy
  • Iryna Didych a:1:{s:5:"en_US";s:118:"Ternopil Ivan Pul’uj National Technical University, Faculty of Computer-Information Systems and Software Engineering";}
  • Yuri Lapusta

DOI:

https://doi.org/10.36547/ams.26.1.346

Keywords:

fatigue crack growth, stress intensity factor, stress ratio, neural network, lifetime, machine learning

Abstract

Important structural elements are often under the action of constant amplitude loading. Increasing their lifetime is an actual task and of great economic importance. To evaluate the lifetime of structural elements, it is necessary to be able to predict the fatigue crack growth rate (FCG). This task can be effectively solved by methods of machine learning, in particular by neural networks, boosted trees, support-vector machines, and k -nearest neighbors. The aim of the present work was to build the fatigue crack growth diagrams of steel 0.45% C subjected to constant amplitude loading at stress ratios R = 0, and R = –1 by the methods of machine learning. The obtained results are in good agreement with the experimental data.

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Published

2020-03-19

How to Cite

Yasniy, O. ., Didych, I., & Lapusta, Y. . (2020). PREDICTION OF FATIGUE CRACK GROWTH DIAGRAMS BY METHODS OF MACHINE LEARNING UNDER CONSTANT AMPLITUDE LOADING. Acta Metallurgica Slovaca, 26(1), 31–33. https://doi.org/10.36547/ams.26.1.346

Issue

Vol. 26 No. 1 (2020): Volume 26, No.1, 2020

Section

Technical papers