Damage detection of composite plates by Lamb wave ultrasonic tomography with a sparse hexagonal network using damage progression trends

Lamb wave based structural health monitoring shows a lot of potential for damage detection of composite structures however, currently there is no agreed upon optimal network arrangement or detection algorithm. The objective of this research is to develop a sparse network that can be expanded to detect damage over a large area. To achieve this, a novel technique based on damage progression history has been developed. This technique gives an amplification factor to data along actuator-sensor paths that show a steady reduction in transmitted power as induced damage progresses and is implemented with the reconstruction algorithm for probabilistic inspection of damage (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 12mm. 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.