The determination of levels of porosity is important in the engineering uses of graphite fiber/polymer matrix composites, since the interlaminar shear strength can be greatly reduced by excessive porosity [1). Research in making nondestructive evaluations using ultrasonics as the probing energy has taken many directions. Hsu [2) has successfully modeled the frequency dependent attenuation to predict porosity levels in composites. Kline [3) has extended the work of Hashsin and Rosen [4) to determine the porosity and fiber volume fraction of composites by solving for the elastic coefficients of the composite structure. The propagation of leaky Lamb waves [5) has also been used to model porosity levels.
We have developed a simple, effective method for analyzing the transmission and reflection of acoustic signals from the bulk of a composite material.Using a one-dimensional invariant embedding arguement, we derive two coupled recursion relations for the reflection and transmission coefficients and a non-linear identity between them. These recursion relations take the place of the differential equations for the propagation of waves in the medium and, when they are solved, two generic, frequency dependent parameters emerge. These parameters characterize the material and its status.One parameter scales the thickness of the composite and can be identified with the complex wave number describing the dispersion and attenuation within the sample. The other parameter is related to the properties of local scattering centers; every process leading to scattering of the acoustic signal within the bulk of the material contributes to the value of this parameter. We report here preliminary efforts to study this model with a view to developing a procedure for evaluating a composite for damage. We have measured the acoustic signal reflected from and transmitted through a carbon fiber/epoxy resin composite at several points on the sample and from this data derive the scattering parameter. Values of the scattering parameter in undamaged regions of the sample are compared to values in damaged regions.
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