## 1 Introduction
Measurements of the Lamb wave amplitudes or velocities in a noncontact way are commonly used in nondestructive testing of plates, boards, pipes, or more complicated structures made from plastic or metallic materials [12; 19; 20; 21; 22]. The aim of the study was to: (1) assess the presence of defects as well as their size, type, and orientation based on the amplitudes of Lamb ultrasonic waves measured inplates made of polyamide (PA6) and polyethylene (PE), due to their homogeneous internal structure, which mainly determined the selection of such model materials for testing; and (2) verify the possibilities of building automatic quality control and defect detection systems based on ML based on the results of the above-mentioned studies within the Industry 4.0/5.0 paradigm. The ultrasonic tests were performed for three types of selected defects found in the structure of board materials: delamination at the board edge, cracking at the board edge, and cracking (discontinuity) in the middle part of the board. The synthetic defects, resembling those occurring in real boards, were applied in a controlled manner, and their size varied from 1 mm to 15 mm. The study's originality lies in detecting the defect's presence close to the board edges, i.e., in the areas where they mostly appear during production or transportation. This issue is often omitted in the literature due to the problematic interpretation of the results of ultrasonic tests because, in these areas, two overlapping effects are observed: (i) reflection of the Lamb wave from the edge and (ii) diffraction of the Lamb wave on defects. As a result, significant changes in the amplitudes of the recorded signals are observed as a function of the scan position at the beginning and end of the plates, while they are not observed in the central parts of the plates. The effect of the boundary disappears with distance from the plate edges and in the central parts, the wave amplitudes are almost constant. Accessing more precise information about defects' characteristics, such as the shape, length, depth, etc., is quite challenging because the effects of the Lamb wave's reflection from the edges overlap with the effects of the wave diffraction on the defects. So far, other authors have conducted research in the middle part of the boards, where the effect of the propagating Lamb wave's interference with the board's edges is very small or negligible [12; 22; 23; 24]. The authors have not encountered in the literature the application of air-borne ultrasonic measurements of Lamb waves in plastic plate-like materials (characterized by high attenuation) for relatively long distances (about 1 m and even longer distances). The contactless method has the potential for industrial applications for quality control of various plate-shaped materials (composites, plastic, wood-based, metallic boards) even in motion (e.g., during production), provided it is correctly scaled. In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method's potential for building cyber-physical systems and digital twins [25]. AI/ML-based quality control in Industry 4.0 and Industry 5.0 offers many benefits, such as real-time defect detection, reduced waste, and improved productivity by predicting maintenance needs and improving overall product quality [26]. These systems can handle large amounts of data, providing deep insights into production patterns and automating repetitive tasks, reducing human error. However, reliance on AI/ML also introduces drawbacks, including high initial implementation costs, technical complexity, and the need for specialist skills to manage and maintain these systems [27; 28; 29; 30; 31]. In addition, AI systems may struggle with highly personalized or novel scenarios, where human judgement still plays a key role, as highlighted in Industry 5.0 [32; 33]. Finally, potential ethical concerns regarding data and algorithm transparency must be considered when implementing AI/ML-based quality control [34; 35].