Damage characterization of carbon/carbon laminates using neural network techniques on AE signals

Abstract

Carbon/Carbon (C/C) composites have a large number of microcracks in matrix-matrix or fiber-matrix regions resulting from the thermal processes during manufacturing. Although not harmful to the overall structural integrity, such a network of microcracks creates a noisy background from early load application which ‘covers’ the onset of critical failure mechanisms. Conventional AE analysis based on a sudden activity increase observed in the cumulative events versus load plot, or amplitude distribution histograms, provides limited information on this type of AE activity. Instead, multivariate techniques of unsupervised pattern recognition, taking into account a large number of AE signal descriptors, are proven useful for the clustering of similar AE events. AE results from a systematic fracture mechanics study of 2D woven C/C laminates are analyzed in this paper. Artificial Neural System (ANS) methods are employed for the clustering of similar AE signals, enabling a phenomenological correlation with the actual failure modes. The numerical procedure introduces a modified Learning Vector Quantization (LVQ) technique which was proved fast and suitable for the type of AE data emitted by composites. Fractographic evidence from failed tensile coupons corroborates the predictions of the numerical method in recognizing different failure mechanisms. Cumulative event charts of the various classes versus load demonstrate the criticality of each class on final coupon failure, and may lead to the definition of reliable criteria for the evaluation of remaining strength or life.