A fatigue test based on inclined loading block concept to benchmark delamination growth considering loading history and R-curve effect

I. Leciñana, J. Renart*, L. Carreras, A. Turon, J. Zurbitu, B. H.A.H. Tijs

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

14 Downloads (Pure)

Abstract

The main objective of this paper is to present a delamination benchmark test concept for composite materials that develop non-self-similar delamination in characterization specimens. The non-self-similar delamination is induced by rotating the loading blocks. The simplicity of the test allows for analyzing the loading mode history by concatenating different loading conditions, such as static and fatigue loading, under multiple loading modes. The methodology introduced in this paper can be particularized for any given composite material set and any sequence of loading conditions. To demonstrate the capabilities of the benchmark test, a case study is presented using AS4D/PEKK-FC thermoplastic composite material, which exhibits strong R-curve behavior. A sequence of opening and shear failure modes was applied under static and fatigue loading, providing an experimental data set that is ready to be used as a part of the validation of numerical predictive delamination models. The delamination process was monitored by X-ray radiography, and the final fracture surfaces were analyzed with scanning electron microscopy (SEM), giving a physical insight into the contribution of the fracture mechanisms to the delamination process.

Original languageEnglish
Article number108128
Number of pages16
JournalComposites Part A: Applied Science and Manufacturing
Volume181
DOIs
Publication statusPublished - 2024

Keywords

  • Benchmark validation test
  • Delamination process zone
  • Fatigue
  • Loading history
  • R-curve effect

Fingerprint

Dive into the research topics of 'A fatigue test based on inclined loading block concept to benchmark delamination growth considering loading history and R-curve effect'. Together they form a unique fingerprint.

Cite this