Internal Damage Identification of Sandwich Panels With Truss Core Through Dynamic Properties and Deep Learning

Abstract

For sandwich panels with truss core, the weakest part is the low-density core; therefore, some effective damage identification methods have been previously proposed for sandwich panels. However, these studies have mainly focused on damage location identification and only a few studies have discussed detection of the extent of the damage. In this study, a damage identification method integrating a deep learning technique with dynamic properties is proposed to identify both the location and extent of internal damage in sandwich panels with truss core. An analytical model verified by experiments based on a laser vibrometer is used to obtain raw data, which can generate various levels of damage inside the two face sheets. Instead of using surface photographs or raw data as the deep learning training dataset, the dataset is constructed using damage indices. By combining this with an analytical model, a dataset of specimens with various defects was collected and used as the input for the neural networks. The ability to identify the locations of damage and the extent of damage was used to evaluate the effectiveness of the proposed technique. The results show that the proposed method could be used to identify the location and extent of internal damage accurately.