Numerical-Experimental Investigation into the Tensile Behavior of a Hybrid Metallic–CFRP Stiffened Aeronautical Panel

In this work, the tensile behavior of a hybrid metallic–composite stiffened panel is investigated. The analyzed structure consists of an omega-reinforced composite fiber-reinforced plastic (CFRP) panel joined with a Z-reinforced aluminum plate by fasteners. The introduced numerical model, able to simulate geometrical and material non-linearities, has been preliminary validated by means of comparisons with experimental test results, in terms of strain distributions in both composite and metallic sub-components. Subsequently, the inter-laminar damage behavior of the investigated hybrid structure has been studied numerically by assessing the influence of key structural subcomponents on the damage evolution of an artificial initial debonding between the composite skin and stringers.

Impact Detection on Composite Plates Based on Convolution Neural Network

This paper presents a novel Convolutional Neural Network (CNN) based metamodel for impact detection and characterization for a Structural Health Monitoring (SHM) application. The signals recorded by PZT sensors during various impact events on a composite plate is used as inputs to CNN to detect and locate impact events. The input of the metamodel consists of 2D images, constructed from the signals recorded from a network of sensors. The developed meta-model was then developed and tested on a composite plate. The results show that the CNN-based metamodel is capable of detecting impacts with more than 98% accuracy. In addition, the network was capable of detecting impacts in the other regions of the panel, which was not trained with but had similar geometric configuration. The accuracy in this case was also above 98%, showing the scalability of this method for large complex structures of repeating zones such as composite stiffened panel.