Development of ANN for delamination detection in composite laminates using Lamb waves

Antonio Fernández-López, Daniel Del rio-velilla, Fernando Sanchez-iglesias

Lamb waves have been long time used for damage detection due to their high sensitivity to interference in wave propagation and high damage coverage with a small number of sensor. However, due to the different wave speeds of multiple Symmetric and Antisymmetric modes, wave dispersion, and numerous reflections and losses introduced by the geometry, suh boundaries, reinforcements, and thickness changes. For the previousmentioned reasons, Lamb wave Structural Health Monitoring (SHM) techniques application is limited due to the difficult data processing required for an accurate damage location and characterization. Even if it is possible to find a variety of methodologies and signal processing techniques to deal with damage detection, techniques based on Deep Learning (DL) has taken special relevance. Specifically, an Artificial Neural Network (ANN) model based on a Multi-Layer Perceptron (MLP) it is proposed to determine both the location and size of structural defects. In this case, the ANN input consists of specific characteristics extracted from the differential signal (damaged state vs undamaged state), such as the number of wave packets, maximum packet value, and amplitude, for a set of different locations, delamination areas and severities. This methodology proves to be highly effective, achieving a small localization error smaller than the size of the damage itself. Even if the high potential of this DL technique, its benefits require a complex training, as large datasets are required, which are unfeasible to obtain solely from experimental setups, so an extensive application of simulations are required. The use of traditional Finite Element Methods (FEM) are not feasible, as they demand significant computational resources and time; other specific techniques such Spectral Element Mehod (SEM) of Physic Based ANN presents high difficulties to implement out-of-plane reinforcements or geometric changes. This work applies the validated and computational effective Lamb wave simulation technique based on the Ray Tracing (RT) to simulate the damage response in a huge range of cases required to apply MLP ANN for SHM. RT technique enables precise modeling of wave reflection, refraction, damping, and mode conversion at structural discontinuities, and reduce the each simulation case to few minutes instead of several hours. It has been applied to a representative aerospace structures, such as UAV wing lower cover made of composite material. This structure presents change of thickness and a cobonded stiffeners. It is proposed to validate the technique with real Barely Visible Impact Damage (BVID) and high energy impacts in different locations to demonstrate the potential of the technique as well the RT training simulation method.