## 1 Introduction  
In recent years, there has been an increase in the use of adhesive joints in many industries. They are attractive due to their ability to bond different classes of materials, such as metals and composites, while uniformly distributing stress and being lighter than conventional joining methods, namely rivets [1; 2; 3]. Different types of adhesive joints can be manufactured, the single-lap joint (SLJ) being one of the most-used, with applications ranging from the automotive to the aeronautical industries [4]. Although the adoption of adhesive joints is widespread, their use in critical primary structures is still limited due to their inability to meet the needed strict certification and inspection criteria [5]. Existing standards require that, for any joining mechanism, there must exist reliable and robust nondestructive testing (NDT) methods for damage detection. Current NDT methods have strong limitations with adhesive joints, and certain types of damage cannot be effectively detected [5; 6; 7], as is the case of weak adhesion. Weak adhesion is characterized by a reduction of at least 20% of the maximum adhesive strength of the joint, without presenting any discernible voids, cracks, or other visible alterations in the structure. Weak adhesion is normally caused by contamination between the adhesive and the substrate and is commonly due to the presence of oil or dust or improper surface preparation for the desired adhesive [8]. Some NDT methods have been proposed for detecting weak adhesion. The one that has recently shown the most-promising results involves Lamb waves (LWs). LWs are a form of elastic disturbance that propagates trough thin plates with shallow to no curvature and possess the capacity to travel long distances without large signal attenuation [9; 10]. These properties come with the disadvantage that the raw signals are extremely complex, and depending on the frequencies and materials used, they are multimodal, which further complicates any required type of analysis [11]. This complexity can be mitigated by choosing appropriate methods to evaluate and process the LW data. One such option is the use of machine learning methods applied to the time series, as they allow a hands-off approach for detecting and classifying any damage present in the bonded structures. For using machine learning, large datasets are often needed for training and testing the algorithms. Creating large datasets experimentally is costly or even impracticable. Therefore, developing numerical models of the SJ and, subsequently, validating them with experimental data reduce the overall burden. An added benefit is that any large volumes of data can be obtained to develop machine learning algorithms. This paper proposes an NDT technique based on LW data and artificial intelligence algorithms for detecting weak adhesion in SLJs. Initially, a dataset was generated by simulating LWs propagating in an SJJ subjected to multiple levels of weak adhesion. This was accomplished by a finite-element model (FEM) developed in the software Abaqus (Dassault Systemes, Velizy-Villacoublay, France), which generated over 1000 variations of weak adhesion at the SLJ. The numerical data were validated with experimental tests, for the sake of reliability, thus forming the base of the present work. The raw data were preprocessed to remove outliers and generate normalized time series. These processed data were then used in 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). These data-driven algorithms were chosen as they are widely used for time series forecasting, while still being commonly used in other non-time related datasets. The algorithms were adjusted and compared in terms of accuracy and training burden.