Locating Defects in Anisotropic CFRP Plates Using ToF-Based Probability Matrix and Neural Networks

This paper presents two algorithms, both based on the time of flight (ToF) of scattered waves, to locate defect in an anisotropic woven-fabric carbon fiber reinforced polymer (CFRP) plate. The first algorithm uses a probabilistic approach by constructing a probability matrix. Each element of this matrix is associated with a location and represents the probability of existing a defect at the corresponding spatial coordinates of the plate. For the probability matrix method, localization results are influenced by manually chosen parameters and by the anisotropy of the CFRP plate. The second algorithm, based on artificial neural networks (ANNs), enables us to improve the accuracy of locating defects. With this ANN method, the anisotropic feature can be "learned" by the neural network from training data. The ToF of scattered waves obtained from three sensor pairs were used directly as inputs of the neural network. The spatial coordinates of the defect are the ANN outputs. The difficulty of obtaining sufficient experimental data for ANN training was surpassed by using added blocks on the surface of the plate under test to simulate defects. This scheme was validated and proved to be effective by comparing the scattered waves from a delamination and from an added block. Conclusions have been drawn by comparing the two methods. The localization results obtained by the ANN algorithm are proved to be better.