## 1 Introduction  
In the last few years, the utilization of adhesive joints has seen an increase in various industries. This is attributed to their ability to connect different materials while providing uniform stress distributions around the joining areas, as well as to create structures that are lighter than those obtained with traditional joining methods [1; 2; 3]. Examples of industries that have benefited from the use of adhesives are the automotive manufacturing industry, with adhesive bonds in body panels, interior components, and even in joining chassis parts, allowing for cars to be built in fewer parts and thus making them lighter and cheaper [1], and the healthcare industry, with delicate applications for securing catheters to human skin, or even for attaching orthopedics to bones [4]. Other industries that can be mentioned are the renewable energy sector, with the fabrication of efficient and easier-to-install solar panels, the marine industry, with the fabrication of lightweight hulls and deck components,and the packaging industry, where adhesive tapes have been heavily relied upon to secure and protect packages [5; 6; 7]. The single lap joint (SLJ) is one of the most used types of adhesive assembles, with applications in different industries, ranging from the automotive to the aeronautical sectors [8]. However, the use of adhesive structural joints in cutting-edge industries has been restricted due to the high certification and inspection requirements posed by industry standards, as is the case for the Federal Aviation Regulations (FAR) Section 23.573(5)-(iii) [9]. Indeed, these standards demand reliable and robust non-destructive testing methods (NDT) to detect damage in joined parts, but existing testing methods for adhesive joints have great limitations and are inadequate in detecting some types of damage [9; 10]. One of the primary types of damage that affects adhesive joints, for which there is a lack of suitable NDT methods, is weak adhesion. This defect is present when the maximum adhesive strength of the joint decreases more than 20% without showing any noticeable voids, cracks, or other visible changes. Usually, this happens when, during the manufacturing process, there is contamination between the adhesive and the substrates, which is often caused by oils, dust, or a poorly prepared surface that does not allow the adhesive to adhere properly [11]. To detect weak adhesion, several methods have been proposed, one of the most promising being based on the use of Lamb wave (LWs) data. LWs are a kind of guided wave that have the ability to travel over long distances and to sensitively interact with structural defects, meaning that their characteristic parameters change in the presence of defects, even in parts with complex geometries, making them a powerful tool in the realm of NDT [8]. The major drawback to their application is the complexity of the signals produced, which poses challenges to correlating LW with damage. To counter this problem, the implementation of robust methods, such as those based on machine learning, is needed. While traditional signal-processing approaches may struggle when dealing with low signal-to-noise ratios or sparse data acquisition points, machine learning algorithms can effectively process complex information and still yield valuable insights. As more data are collected from different structures and inspection scenarios, the algorithms can continually refine their understanding of complex LW data, enabling them to better discriminate between normal wave patterns and those associated with defects. When using machine learning, large datasets are often needed to train and test the algorithms. Creating large datasets experimentally is costly or even impracticable. Therefore, developing reliable numerical models of the SLJs, validated with experimental data from real parts, can reduce the overall burden [12]. An added benefit is that any large volumes of data can be obtained to develop machine learning algorithms.