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Journal of Materials Science

, Volume 51, Issue 24, pp 10793–10805 | Cite as

Hygrothermal effects on fatigue behavior of quasi-isotropic flax/epoxy composites using principal component analysis

  • Fabrice Kossi Sodoke
  • Lotfi ToubalEmail author
  • Luc Laperrière
Original Paper

Abstract

This work studies the long-term hygrothermal (HT) aging effect on the fatigue behavior of a flax/epoxy bio-composite arranged in [02/902/±45]S lay-ups. The effect of aging on static tensile mechanical properties was first investigated. Tension–tension fatigue tests were also performed for both unaged and aged samples. The distribution of fatigue life for both unaged and aged sample was determined. The evolution of fatigue properties was also investigated. Fatigue tests were coupled with acoustic emission (AE) for a better understanding of how these composites react to fatigue loading in wet environmental conditions. Static tests show that water absorption affects negatively the elastic properties of this material. S–N curves show a good performance in fatigue strength of unaged samples. This performance dropped significantly with HT aging. The analysis of stress–strain hysteresis loops allowed to determine the minimal strain such as the desirable fatigue properties to explain fatigue damage evolution for single stress component. Hwang–Han’s model based on minimal strain was also used to predict the fatigue damage of the tested flax/epoxy composites. Principal component analysis enabled to separate the fatigue damage evolution of unaged and aged samples. AE results confirmed that the damage evolution of both samples is not the same. AE analyses were permitted to identify the growing fiber/matrix debonding and pull-out mechanism in the unaged samples. Correlation between AE and scanning electron microscope observations enabled the identification of several damage mechanisms and their evolution during the fatigue tests.

Keywords

Fatigue Acoustic Emission Fatigue Life Fatigue Behavior Damage Evolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to acknowledge the financial support of the natural sciences and engineering research council (NSERC) of Canada through its discovery Grant Numbers 138039–2012 and 386284–2010.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Laboratory of Mechanics and Eco-Materials, Mechanical Engineering DepartmentUniversity of Quebec at Trois-RivièresTrois-RivièresCanada

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