## 2 Methods and Materials
### Non-Destructive Testing (NDT) Techniques
However, one of its primary limitations is the relatively shallow detection depth compared to other inspection techniques discussed earlier. As described by [PERSON] et al. [48] infrared thermography (IRT) is primarily classified into \"passive\" and \"active\" thermography. Passive IRT does not require an external energy source and is typically used when the object being inspected has a significant temperature difference from its surrounding environment. This method is particularly useful for applications involving cyclic loading, as the heat generated during these cycles allows for monitoring temperature variations over time with an infrared (IR) camera. A notable example of passive thermography is the work by [PERSON] et al. [49], who used this technology to study the fatigue behaviour of carbon fibre-reinforced polymer composites through infrared imaging. On the other hand, Active IRT relies on external excitation sources to create a temperature difference between damaged and undamaged regions of the material being tested. In aerospace applications, the most commonly used active IRT methods based on optical radiation principles include Pulsed Thermography (PT), Lock-In Thermography (LIT), Step-Heating Thermography (SHT), Long-Push Thermography (LPT), Frequency Modulated Thermography (FMT), Laser-spot Thermography (LST), and Laser-line Thermography (LLT) [39]. Table 2 provides a detailed summary of each method, outlining the type of external optical heat source, heating duration, penetration depth, primary applications, and the types of defects in aircraft that each method is best suited to detect. [PERSON] et al. [50] introduced multi-label classification algorithms designed to predict multiple factors simultaneously from thermal images gathered through active thermography, specifically Pulsed Thermography (PT). A total of 24,000 thermal images were captured under various conditions using different geometrically shaped composite material specimens, which were then used to train machine learning models for classification. The measurement system included an X6900 FLIR (Teledyne FLIR LLC, Wilsonville, OR, USA) infrared camera equipped with an InSb-CCD Matrix Sensor, two Fx60 (Balcar, France) photographic flashes that generated a heat flux for a duration of 2 milliseconds, a control unit to synchronise data acquisition with pulse generation and a data processing unit (Figure 6). The evaluation results showed that the proposed methods performed well in making accurate predictions. The findings suggested that the approaches used, like the RF MLC model (Random Forest multi-label classification) and the use of statistical features in the dataset, were very promising options for classifying a comparative investigation for the non-destructive testing of honeycomb structures by holographic interferometry thermal images of composite materials. Figure 5: Graphical representation of the AE transfer function [47]. A newer option for non-destructive testing (NDT) in aircraft maintenance is 3D scanning [19; 20; 40], which uses laser or LiDAR (Light Detection and Ranging) technologies. This method effectively detects surface defects like dents, wear, deformation, and corrosion. Its benefits include being non-contact, highly accurate, and fast in collecting data. It also works well with different materials and shapes, producing detailed 3D models. However, it is limited to detecting surface defects, the equipment can be expensive, and environmental factors like reflections can affect the results. Three-dimensional technology has already been applied across many industries, improving medical diagnosis [51; 52], aiding in the preservation of historical assets [53], supporting maintenance in manufacturing [54], enriching STEM education [55] and benefiting numerous other fields. Three-dimensional laser scanning involves using a laser beam to precisely capture the shape and geometry of a real-world object in digital form [56].