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Journal of Materials Science

, Volume 51, Issue 6, pp 3277–3288 | Cite as

Characterization of 3D woven reinforcements for liquid composite molding processes

  • H. Alhussein
  • R. Umer
  • S. Rao
  • E. Swery
  • S. Bickerton
  • W. J. Cantwell
Original Paper

Abstract

Compaction and permeability characterization of fibrous reinforcements is fundamental to liquid composite molding (LCM) processes, given that these fabric properties determine the part thickness, fiber volume content and mold filling time and patterns. In this study, the permeability of three different 3D woven carbon fiber reinforcements (orthogonal, angle interlock and layer-to-layer) was studied, each having a different weave style and z-binder pattern. For all reinforcements, single-cycle and multiple cycle compaction experiments were conducted on dry and saturated samples. The orthogonal preforms were more difficult to compact to the target fiber volume fraction of 0.65, with peak stresses reaching up to 2.3 MPa. Cyclic compaction tests were conducted to highlight the importance of permanent deformation in the reinforcements, when higher fiber volume content is desired via manufacturing using an LCM process. Unsaturated in-plane radial and saturated through-thickness permeability data were obtained at several fiber volume fractions. The orthogonal and layer-to-layer fabrics exhibited greater levels of anisotropy in the plane of flow. The rate of change of in-plane permeability with increasing fiber volume content was lower compared to through-thickness values. The effect of cyclic compaction on permeability was greater in the orthogonal and angle interlock reinforcements. Micrographs of infused samples showed significant permanent deformation of the z-binder yarns, an effect that reduced permeability at high fiber volume contents.

Keywords

Compaction Fiber Volume Fiber Volume Fraction Permanent Deformation Flow Front 
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 thank Mr. Jimmy Thomas, Mr. Bittu Scaria and Mr. Mohammed Rashed Abdulla Alsuwaidi at Khalifa University for their assistance in conducting the experiments. The authors gratefully acknowledge Mubadala Aerospace and Khalifa University for providing the financial support for this research project.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • H. Alhussein
    • 1
  • R. Umer
    • 1
  • S. Rao
    • 1
  • E. Swery
    • 2
  • S. Bickerton
    • 2
  • W. J. Cantwell
    • 1
  1. 1.Aerospace Research and Innovation CenterKhalifa UniversityAbu DhabiUAE
  2. 2.Center for Advanced Composite Materials (CACM)The University of AucklandAucklandNew Zealand

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