Identification of failure modes in composite materials

Abstract

Recently a new structural health monitoring system that employs a acoustic emission and an embeddable local processor has been proposed. The development of a processor that integrates the functions of signal conditioning, feature extraction, data storage, and digital communication is currently in progress. A prototype of this local processor chip has been developed. The integration of a continuous sensor with an embeddable local processor can potentially enable an inexpensive method of monitoring large and complex structures using acoustic emission signals. Such a system can reduce the cost, complexity, and weight of the required instrumentation. It is potentially scalable to large and complex structures and could be integrated into the structural material. The success of the acoustic emission based structural health monitoring technique depends on its ability to discriminate between valid acoustic emission signals and ambient noise. In addition, the technique should be able to identify the damage mode from the acoustic emission waveforms. This paper focuses on the use of acoustic emission technique for the identification of failure modes in composite materials. Three types of failure modes in glass fabric epoxy composite laminates are considered. These are two types of delamination growth and transverse crack growth. Wavelet analysis is used to extract time frequency information from the acoustic emission signals. Different features of the waveform including the frequency components, Symmetric and Antisymmetric components, and amplitudes are used to classify the signals and identify the failure modes. The laboratory tests indicate that it is possible to distinguish the individual failure modes under consideration. It was also possible to filter out spurious AE signals that originate from extraneous sources using an appropriate choice of sensors and frequency components. An attempt is made to relate the rate of damage growth with the detected acoustic emission signal parameters.