Buckling and postbuckling behaviour of Glare laminates containing splices and doublers. Part 1: Instrumented tests

Abstract

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Buckling and postbuckling behaviour of Glare laminates containing splices and doublers. Part 2: Numerical modelling

“…In each case sections were taken both in the area of the joint and remotely (Figures 15 and 24) to allow comparison and hence validation of the damage predictions obtained from the FE models developed. Further details of these experiments are discussed in a companion paper [29].…”

“…As shown in Figure 1, artificial delaminations were simulated by introducing 4 mm wide strips of polytetrafluoroethylene (PTFE) film of thickness 10 µm embedded in the splice and doubler structures [29].…”

“…The DIC system provided full-field displacement data based on one of the faces of the specimen, while three AE sensors mounted on the opposite face recorded event energies and locations. The latter were computed using the bespoke location algorithm 'Delta T' described in [44], which has been shown to provide greater location accuracy for complex materials and structures such as Glare R laminates [29] than the standard time of arrival techniques normally used. Following testing, specimens were sectioned and inspected using scanning electron microscopy (SEM) to determine the failure modes present.…”

“…Finally, model predictions are verified based on the results of experimental work including Scanning Electron Microscopy (SEM) micrographs which enable the damage found in different areas of the specimen to be characterised for comparison with that predicted by the model. This work, which is summarised here is expanded in [29].…”

Postfatigue Life Prediction of Glare Subjected to Low-Velocity Impact

“…These fibers create a load transfer pass over the crack and restrain crack opening. Therefore, less force is transferred around the crack tip in metal layers that this, in turn, reduces the stress concentration factor of the crack tip [9][10][11][12][13][14][15][16][17][18]. Considering the present metaheuristic algorithms, the multilayer perceptron neural network was selected for modeling due to their high performance in educability and the possibility of approximating non-linear results.…”