The Master SN curve approach – A hybrid multi-scale fatigue simulation of short fiber reinforced composites

Atul Jain*, Jose M. Veas, Stefan Straesser, Wim Van Paepegem, Ignaas Verpoest, Stepan V. Lomov

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Typical short fiber reinforced composites (SFRCs) components have a different statistical distribution of orientation of fibers at different points leading to different static and fatigue behavior at different locations across the component. To link component-scale calculations with this variability of fiber orientations, each element in the FE model is modeled as a Representative Volume Element (RVE); the static and fatigue properties must be calculated for each of these elements. While there are established methods to estimate the static properties, there are none for the fatigue properties. A hybrid (combination of micromechanics and tests) and multi-scale (damage in micro-scale linked to macroscale fatigue properties) method of predicting the SN curve for every point in a short fiber composite has been developed. This proposed method is based not only on tests but on a combination of manufacturing simulation, tests and multi-scale mechanics. An extensive test program was undertaken to study the fatigue behavior of short fiber composites and validate the concept of the Master SN curve (MSNC) approach. The MSNC approach is compared with two prevalent approaches – strength based scaling and test based interpolation. The MSNC approach was found to be in a good agreement with the experimental results and was confirmed to be more accurate than the prevalent methods.

Original languageEnglish
Pages (from-to)510-518
Number of pages9
JournalComposites Part A: Applied Science and Manufacturing
Publication statusPublished - 1 Dec 2016
Externally publishedYes

Keywords / Materials (for Non-textual outputs)

  • B. Fatigue
  • C. Damage mechanics
  • C. Micro-mechanics
  • E. Injection molding


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