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Lamb wave based structural health monitoring shows a lot of potentialfor damage detection of composite structures. However, currently there is noagreement upon optimal network arrangement or detection algorithm. The objective of this research is to develop a sparse network that can be expandedto detect damage over a large area. To achieve this, a novel technique basedon damage progression history has been developed. This technique gives anamplification factor to data along actuator-sensor paths that show a steadyreduction in transmitted power as induced damage progresses and is implemented with the reconstruction algorithm for probabilistic inspection ofdamage (RAPID) technique. Two damage metrics are used with the algorithm and a comparison is made to the more commonly used signal difference coefficient (SDC) metric. Best case results show that damage is detected within 12 mm. The algorithm is also run on a more sparse network with no damage detection, therefore indicating that the selected arrangement is the most sparse arrangement with this configuration.

Damage Detection of Composite Plates by LambWave Ultrasonic Tomography with a Sparse HexagonalNetwork Using Damage Progression Trends