###### Abstract
Ultrasonics can accurately measure flaw depths, which is valuable for assessing damage severity and predicting future structural performance. These waves can scatter or reflect unpredictably in structures with complex geometries or varying thicknesses, complicating the interpretation of results. Laminated composites are anisotropic, meaning their properties vary in different directions [19]. This variation can affect ultrasonic wave propagation, requiring specialized procedures and calibration for accurate results. A strong connection between the transducer and the material surface is necessary for effective ultrasonic wave transmission. Surface roughness or imperfections can compromise result accuracy. Ultrasonic transducers embedded in composite structures can continuously monitor health without external probes. These devices can provide real-time data on damage progression [20]. Recent advancements in the SHM of composite structures using guided ultrasonic waves have facilitated the integration of various sensor technologies. Piezoelectric wafer active sensors (PWASs) are extensively utilized due to their efficiency in generating and receiving ultrasonic waves. These sensors, often made from materials such as lead zirconate titanate (PZT), are preferred for their high sensitivity and broad frequency range [21]. In addition, fiber Bragg grating (FBG) sensors are used for their precision and immunity to electromagnetic interference, qualities that make them ideal for harsh environments [22]. Capacitive micromachined ultrasonic transducers (CMUTs) and interdigital transducers (IDTs) are also becoming popular for their potential for miniaturization and capability to integrate with electronic systems [23]. These sensors, along with advanced signal processing techniques, facilitate the accurate detection and localization of structural defects, thus ensuring the reliability and safety of composite structures. To evoke the simulation of UT NDT for various materials, one effective approach is the Fundamental Wave Amplitude Difference (FAD) Method [24]. This technique focuses on identifying variations in the amplitude of fundamental ultrasonic waves as they traverse through a material. Such amplitude discrepancies are closely linked to structural anomalies, such as cracks or delamination, allowing for the precise localization and characterization of defects [25]. Machine learning techniques are increasingly being used to improve flaw classification and imaging in non-destructive testing (NDT). Methods such as the Total Focusing Method (TFM) benefit from these advanced approaches to enhance the detection and visualization of defects within materials [26; 27]. These techniques improve the interpretation of wave data, providing more accurate and reliable damage assessments. Additionally, probabilistic imaging methods, which rely on statistical models, complement these efforts by estimating the likelihood and severity of damage with greater precision [28]. Ray tracing reconstruction techniques are used to map the paths of ultrasonic waves through the structure to identify and locate defects. Fusion-based damage diagnostics integrate data from multiple sensors and methods to improve the accuracy and reliability of damage detection. Additionally, the integration of machine learning approaches enables the analysis of complex patterns in the data, thus providing robust damage assessment and predictive maintenance capabilities. Non-destructive testing (NDT) broadly includes both SHM and NDE, focusing on damage-free testing methods. This paper explores how the two approaches are connected and complement each other. We highlight recent advancements, particularly in ultrasonic techniques and sensor technologies, that are enabling more integrated solutions. These advancements help address the limitations of separate SHM and NDE systems, providing more comprehensive strategies for monitoring and maintenance.