Extrinsic Fabry-Perot interferometric (EFPI) fiber optic sensors and a neural network provided a health-monitoring capability for laminated glass/epoxy composite beams. The EFPI sensors experimentally determined the first five modal frequencies of the cantilever beams. The feedforward backpropagation neural network used these modal frequencies to predict the size and location of delaminations in the composite beams. Beam modal frequencies shift as a function of delamination size and location. Five beams with prescribed delaminations, as well as a 'healthy' beam with no delaminations, were excited by a surface-mounted piezoelectric actuator at frequencies up to 1 kHz. All beams had an eight-ply symmetric glass/epoxy composite design, were fabricated simultaneously, and had length and width dimensions of 26.04 and 2.33 cm, respectively. The beams with flaws had different delamination sizes ranging from 1.27-6.35 cm long prescribed in the mid-plane, i.e. between the fourth and fifth plies. The neural network was trained using classical-beam theory and tested using the experimental EFPI data. The delamination size and location predictions resulting from the neural network simulation had an average error of 5.9 and 4.7%, respectively. Also, analytical classical-beam theory, finite element methods, and ceramic piezoelectric sensors validated the EFPI modal frequency measurements.

In-plane strain responses of surface-mounted extrinsic Fabry-P érot interferometric (EFPI) fiber optic strain sensors are investigated. EFPI fiber optic strain sensors are mounted on three graphite/epoxy laminated composite plates. The plates are impacted with various size steel balls using a drop-weight technique. The impacts did not cause apparent damage. The first impact-induced strain peak was characterized by the rise time, peak value, full width at half maximum and decay time. The transient low-velocity impact response of EFPI fiber optic strain sensors is compared to the response from conventional electrical resistance strain gages and polyvinylidene fluoride (PVDF) piezoelectric film sensors. Orientation dependence and other characteristics of the EFPI fiber optic sensing technique are discussed. An in-house finite element program incorporates geometric nonlinearity and transverse shear deformation for the impact events. The finite element results closely match the experimental strain data for the first peak strain response.

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Impact-induced damage in fiber-reinforced laminated composite plates is characterized. An instrumented impact tower was used to carry out low-velocity impacts on thirteen clamped glass/epoxy composite plates. A range of impact energies was experimentally investigated by progressively varying impactor masses (holding the impact height constant) and varying impact heights (holding the impactor mass constant). The in-plane strain profiles as measured by polyvinylidene fluoride (PVDF) piezoelectric sensors are shown to indicate damage initiation and to correlate to impact energy. Plate damage included matrix cracking, fiber breakage, and delamination. Electronic shearography validated the existence of the impact damage and demonstrated an actual damage area larger than visible indications. The strain profiles that are associated with damage were replicated using an in-house finite element code. Using these simulated strain signatures and the shearography results, a backpropagation artificial neural network (ANN) is shown to detect and classify the type and severity of damage.