Minor impacts, perforations, and debonding damages in sandwich composites are studied using two vibration (mode shape curvature and damping matrix identification) and two transient temperature response (geometrical interpretation of convex hulls and thermographic heat patterns) based techniques. This research asserts that the best approach for damage detection in sandwich composites is the strategic combination of complementary damage detection techniques and that a properly trained neural network (NN) scheme can enormously facilitate the diverse data processing and damage source prediction. Although all four techniques resulted in successful damage detection, however, based on the robustness and promise in actual service implementation, only the results of the mode shape curvature and thermographic heat patterns were implemented into a properly trained NN. The article outlines the techniques and methodologies used and lessons learned in the damage detection of sandwich composites.
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