Pattern Recognition for Damage Characterization in Composite Materials

Abstract

*† Composites present additional challenges for inspection due to their anisotropy, the conductivity of the fibers, the insulating properties of the matrix, and the fact that damage often occurs beneath the visible surface. This paper addresses the characterization of damage within composite materials, specifically for structural health monitoring (SHM). Fundamentally, one would like to distinguish between a pristine and damaged structure, however taking a micromechanics view, materials are inherently damaged. Microscopic flaws grow over time, and can be greatly accelerated by events such as overloads or impacts, until a critical damage size is achieved. Therefore a threshold must be introduced, where at some level of detectable flaw size, the structure must be labeled as “damaged”. Using one or two recorded features, such as time or frequency domain measurements, to characterize damage may not be feasible, as they may not be linearly separable. While it may be possible to differentiate between “pristine” and “damaged”, or between 2 discrete damage modes with limited features, it is not possible to separate the entire mode space. Therefore it is necessary to extract several feature sets to allow multi-dimensional classification of damage modes. The presented research utilized Lamb wave testing coupled with principal component analysis and pattern recognition methods, with the goal of providing the presence, type, and severity of damage with a high degree of confidence. Experiments were performed using quasi-isotropic graphite/epoxy laminates with 2 bonded actuator/sensor pairs. Three types of damage were investigated, each at 4 levels of severity: impact, hole and delamination. A total of 9000 datasets were collected in pulse-echo mode at 100kHz. Training data was collected from 1 plate and testing data from the other plates for each damage type. Subsequently, pattern recognition (PR) algorithms were developed to determine presence of damage, as well as to predict the type and severity of damage. These results have shown that PR methods can be used to successfully characterize damage in composites for SHM, with results that would only improve with additional training data.