4.7 Article

Evaluation of multiaxial high-cycle fatigue criteria under proportional loading for S355 steel

Journal

ENGINEERING FAILURE ANALYSIS
Volume 120, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engfailanal.2020.105037

Keywords

Multiaxial fatigue; High-cycle regime; Proportional loading; Damage parameters; Structural steels

Funding

  1. CONSTRUCT Instituto de I&D em Estruturas e Construcoes - national funds through the FCT/MCTES (PIDDAC) [UIDB/04708/2020, UIDP/04708/2020]
  2. FEDER funds through COMPETE2020 (POCI) [POCI-01-0145-FEDER-030103]
  3. national funds (PIDDAC) through the Portuguese Science Foundation (FCT/MCTES)
  4. Wroclaw University Science and Technology
  5. Opole University of Technology (Poland)

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Multiaxial stresses are common in engineering structures, leading to multiaxial fatigue issues with different opinions and models for its description. Experimental research revealed that the multiscale approach by Dang Van and the Susmel model showed high accuracy and correlation in describing the fatigue behavior of S355 structural steel.
Multiaxial stresses are usually present in engineering structures and are often associated to multiaxial fatigue failures. However, multiaxial fatigue is an open topic, full of questions and different points of view. Therefore, an experimental campaign of uniaxial and multiaxial fatigue tests under proportional loading was conducted aiming at evaluating the multiaxial fatigue behaviour of S355 structural steel in the high-cycle fatigue regime. Five different multiaxial models were used and evaluated, namely the Sines, Findley, McDiarmid, Dang Van and Susmel-MWCM. Each of them was applied to experimental data and the mean fatigue curves obtained from it were evaluated and compared. The coefficients present in each model definition were studied and determined through different methods. The Dang Van's multiscale approach and Susmel model showed great accuracy in the description of the fatigue behaviour of the S355 steel, providing the best correlation of the uniaxial and multiaxial experimental data.

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