Abstract

Composite materials are prone to various kinds of defects in their service life, among which delamination is a very hazardous type of damage. The traditional visual inspection techniques often fail to detect delamination in composite structures. Guided Lamb waves are increasingly being applied for the identification of delamination in these structures. Scanning laser Doppler vibrometry can measure the full wavefield of guided Lamb waves, such full wavefield contains rich information about defects. In this research work, a novel deep learning-based semantic segmentation technique is applied for delamination identification on full wavefield data. A big dataset of full wavefield images resulting from the interaction with delamination of random shape, size, and location was utilised and fed into the proposed deep learning model. The main motive of this research work is to investigate the applicability of deep learning-based approach for delamination identification in composite structures by using only the animations of guided Lamb waves. It is verified that the performance of the proposed deep learning model is good. Moreover, it enables better automation of identification of delamination, which can further produce damage maps without the intervention of the user. Furthermore, the developed deep learning model also indicates the capability of generalising well to the experimental data.