In the past decades, Lamb waves (LW) have seen encouraging results when applied to non destructive testing (NDT) and structural health monitoring (SHM), with important research done continuously through the years in both fields. This paper is a comprehensive review on equipment, testing procedures and techniques adopted in NDT and SHM, with emphasis in applications involving SHM of adhesive joints. The fundamentals of LW and their application to damage detection are discussed, and different signal processing, statistical, machine learning and simulation methods are presented. The paper represents a basis to deepen the understanding about the problem at hand and is a thorough compilation of relevant information to future work. Although LW-based SHM methods have been heavily studied, there is a gap of few to no studies between these topics and adhesive joints, namely, for monitoring weak adhesion.

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In recent years, the aeronautical industry has grown with the adoption of new materials for structural applications, and adhesives emerged as an alternative to standard methods to join parts. This has sparked the use of nondestructive testing and structural health monitoring methods, namely, those based on Lamb waves to assess the integrity of structures and detect damage. Methods using time-series sensor data and machine learning algorithms have shown great promise in classifying the extent of damage in plate-like structures. Despite this, robust methods are still missing for choosing relevant features of Lamb waves that optimize the learning process to classify damage. In this paper, a powerful time-series specialized feature extraction method is implemented to detect and classify weak adhesion, meaning a zero-volume defect denoting any intermediate level of adhesion between complete adhesion and kissing bond. Initially, over 75 different types of features, with varying internal parameters, are extracted from raw data. Then, using the Benjamini-Hochberg procedure, some of these features are selected as relevant for the damage classification problem. After the initial selection, the features are handled with machine learning techniques, namely the Naïve Bayes and random forest classifiers, which not only lead to high classification metrics using all features, but also reveal and isolate those features that yield the best differentiation between damage categories. The selection methodology accounts for robustness by utilizing different layers of selection and classification, validating the feature relevance in relation to the appropriate set of classes. As such, different damage types and ranges can be adopted in the proposed multi-class classification pipeline.

In the last few years, the application of adhesive joints has grown significantly. Adhesive joints are often affected by a specific type of defect known as weak adhesion, which can only be effectively detected through destructive tests. In this paper, we propose nondestructive testing techniques to detect weak adhesion. These are based on Lamb wave (LW) data and artificial intelligence algorithms. A dataset consisting of simulated LW time series extracted from single-lap joints (SLJs) subjected to multiple levels of weak adhesion was generated. The raw time series were pre-processed to avoid numerical saturation and to remove outliers. The processed data were then used as the input to different artificial intelligence algorithms, namely feedforward neural networks (FNNs), long short-term memory (LSTM) networks, gated recurrent unit (GRU) networks, and convolutional neural networks (CNNs), for their training and testing. The results showed that all algorithms were capable of detecting up to 20 different levels of weak adhesion in SLJs, with an overall accuracy between 97% and 99%. Regarding the training time, the FNN emerged as the most-appropriate. On the other hand, the GRU showed overall faster learning, being able to converge in less than 50 epochs. Therefore, the FNN and GRU presented the best accuracy and had relatively acceptable convergence times, making them the most-suitable choices. The proposed approach constitutes a new framework allowing the creation of standardized data and optimal algorithm selection for further work on nondestructive damage detection and localization in adhesive joints.

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