Influence and Compensation of Temperature Effects for Damage Detection and Localization in Aerospace Composites

Experiments and modelling of ultrasonic waves in composite plates under varying temperature

Guided wave (GW) structural health monitoring (SHM) systems offer an attractive solution as an in-situ quasi real-time assessment of structural damage, but their sensitivity and efficiency may be impaired under varied environmental and operational conditions. Thus, virtual tests, such as that based on the Finite Element (FE) method, represent a valid approach for simulating and investigating SHM systems, enabling a substantial reduction in experimental campaigns. In this work, GW propagation characteristics in a carbon fibre-reinforced composite plate are studied under a varying temperature condition, representative of the aeronautics application. At first, GW SHM system was physically tested at room temperature (20^°C), and the results were used to calibrate and assess the proposed FE modelling approaches, characterised by different element types and mesh sizes. A temperature independent averaged time compensation factor is proposed to mitigate the numerical data dependency on excitation frequency and propagation angle. Two temperature variations (from 20^°C to -50^°C, and 20^°C to 65^°C) were experimentally and numerically considered to investigate the effect of varying temperature on the GW. For all test cases, the compensated numerical data was compared to the experimental results, and discussed in terms of dispersion curves, focusing on the zero-order symmetric, S₀, and antisymmetric, A₀, modes. Results show that both 2D and 3D FE approaches can accurately predict the changes in GW due to varying temperature, with the group velocity of the A₀ mode being less sensitive to temperature variations.

Damage Localization in Composite Plates Using Wavelet Transform and 2-D Convolutional Neural Networks

Nondestructive evaluation of carbon fiber reinforced material structures has received special attention in the last decades. Usage of Ultrasonic Guided Waves (UGW), particularly Lamb waves, has become one of the most popular techniques for damage location, due to their sensitivity to defects, large range of inspection, and good propagation in several material types. However, extracting meaningful physical features from the response signals is challenging due to several factors, such as the multimodal nature of UGW, boundary conditions and the geometric shape of the structure, possible material anisotropies, and their environmental dependency. Neural networks (NN) are becoming a practical and accurate approach to analyzing the acquired data using data-driven methods. In this paper, a Convolutional-Neural-Network (CNN) is proposed to predict the distance-to-damage values from the signals corresponding to a transmitter-receiver path of transducers. The NN input is a 2D image (time-frequency) obtained as the Wavelet transform of the acquired experimental signals. The distances obtained with the NN are the input of a novel damage location algorithm which outputs a bidimensional image of the structure's surface showing the estimated damage locations with a deviation of the actual position lower than 15 mm.