Damage analysis of Nomex honeycomb sandwich structures using image processing and artificial intelligence approaches

Abstract In this study, a method was developed to detect impact damage and determine the damage propagation in increasing and repetitive impacts. The effect of nanographene additives on the propagation of delamination damage in the top face layer of a sandwich structure was investigated using the developed method. In addition, the effects of nanographene on visible (VID) or barely visible damage (BVID) formation were investigated, and the stiffness of the sandwich structures was evaluated. An ultrasonic C‐scan nondestructive testing was used to visualize the damage, and the numerical values of the damaged areas of the sandwich structures at different energy levels were determined using an image processing method. The obtained values were used in artificial neural network (ANN) training to estimate the impact energy at which the structural integrity of the sandwich structures was disrupted. It was observed that the nanographene additive was effective in maintaining rigidity of the sandwich structures and therefore, slowed the delamination damage progression in increasing and repeated impacts. The damage areas of the neat sandwich structures (NSS) and graphene nanoplatelets doped sandwich structures (GSS) structures at the first impact 5j energy level was 1004 and 811 mm², respectively. At the first impact, 20% less damage occurred in the GNP‐doped sandwich structures. The increasing and repetitive last impact energies at the end of the neural network training were 5j + 35j in NSS and 5j + 50j in GSS. The method allows for a detailed examination of the damage progression of sandwich structures under increasing and repeated impact loads. Highlights Low‐velocity impact tests of Nomex honeycomb sandwich structures were performed. Damage formation and propagation in sandwich structures were investigated. The non‐destructive test C‐scan method was used to determine BVID. The damaged areas were determined using an image processing method. The damage areas were estimated using artificial neural networks.