Quantitative Description of Damage Evolution and Property Degradation of Fiber Reinforced Composite Materials

Abstract

Fiber reinforced composite materials are materials are increasingly being used in many applications. However, characterization and prediction of their long term behavior is still an active area of research. Composite inherently heterogeneous and go through a complex process of material state changes. Their functional life depends upon characterizing and predicting evolution of local details (e.g. distributed damage initiation, accumulation, and interaction) which affect global property (strength and stiffness) degradation and eventual failure. The lack of understanding of those local changes often results in empiricism, limiting innovation in the use of composite materials. Importantly, the intermediate, precatastrophic stage of damage development when isolated defect sites accumulate and begin to form incipient fracture paths is especially difficult to characterize. This proposed research aims at improving material level understanding of the formation of fracture path. In this paper, broadband dielectric spectroscopy (BbDS) has been utilized to capture material state changes due to degradation in composite materials. This multi-physical response of composite materials from BbDS provides important features which relate to degradation behavior and associated loss of properties (strength and stiffness) due different applied conditions. In this study, multiphysical description of material state change has been discussed in two example cases: i) stages of incipient fracture path formation due to cyclic bending, and ii) stiffness evolution under tensile fatigue loading. A 3D X-ray microscope has been used to visualize and hence validate incipient fracture path formation. Validated results from these test cases show that degradation process, and evolution of properties can be characterized with a high sensitivity using multi-physical response of composite material. These results can potentially help enhance predictive modeling. Details of experimental methods, and results are included in the paper.