An edge-reflection minimum variance method of Lamb waves for damage contour evaluation

Abstract Array imaging methods combined with a Lamb-wave transducer network are widely used for damage localization in nondestructive testing and structural health monitoring. Conventional Lamb wave imaging methods including delay-and-sum, sparse reconstruction, and multiple signal classifications are based on the a priori assumption of small-scale damage. Notably, several types of structural damages (e.g. delamination in composite laminates) are large with multiple discretized edges. However, this is an unreasonable assumption that will result in the performance reduction of damage imaging. To boost the imaging performance, an edge-reflection imaging method is proposed in this study. For damages with nonnegligible sizes, reflections occur at different edge positions from different transmitter–receiver pairs in the transducer network. On this basis, the amplitude superposition of array signals is calculated by a modified time shifting rule to highlight damage edges for both damage localization and contour evaluation. During amplitude superposition, a minimum variance algorithm is used to further enhance the edge recognition accuracy. Compared to conventional methods, the imaging performance is improved by the presented method. An experimental investigation is conducted on composite laminates, and the findings demonstrate the high accuracy and performance improvement of the proposed method.