## 1 Introduction
Many other researchers suggest the use of different ultrasonic methods for the detection of interface defects in multi-layered structures, including guided waves, laser ultrasonics, and non-linear ultrasonic evaluation [13; 21; 22; 23; 24]. [PERSON] et al. proposed a novel approach based on the classical pulse echo technique and machine learning for the classification of weak bonds [25] As a result, ultrasonic techniques are extremely popular and more suitable for the inspection of multi-layered structures for detecting debonding-type defects. X-ray radiography can offer significant spatial resolution and can investigate the overall thickness of the adhesive. For this reason, X-rays can image and quantify voids over the entire adhesively bonded joint, being an attractive alternative for different inspection techniques [26]. However, in terms of feature detection, contrast is required [27]. Good contrast is achieved when the attenuation coefficient of the X-rays differs significantly in the material inspected in contrast to the defect and/or there is a significant difference in the propagation path length of X-rays in the various materials (defect/non-defect) [28]. Therefore, X-ray radiography is especially good for the detection of volumetric defects, such as large voids [29] or inclusions. In the case of the inspections of thin structures, such as lap joints, where the thickness of the adhesive is small, this poses difficulties in detecting the defects in the joints where thickness along the propagation path of the X-rays is negligibly small (such as in the case of disbonds) and where the attenuation of X-rays in good/bad joints are almost the same. In addition, it is difficult to detect interface defects with high reliability, since there are many influencing factors depending on the techniques used, samples under investigation, and interface defect types. Therefore, it is significantly important to select and develop a high-performance NDT technique that will satisfy the requirements of testing adhesive joints for high-reliability internal defect detection. The limitation of identifying appropriate NDT techniques has had a considerable influence on the general usage of adhesives, as it is widely assumed that if NDT techniques were reliable enough to ensure bond line integrity, adhesive bonding would be utilized significantly more than it is now. Given that each non-destructive evaluation technique has varying sensitivity to different defects depending on the component inspected, multiple non-destructive evaluation methods are frequently applied to achieve improved, more reliable, and precise results, demonstrating the need for data fusion of multiple techniques [30]. [PERSON] published the first book on the applications of NDT data fusion [31]. It provides a variety of research studies reflecting on how multi-sensor NDT is competent to integrate redundant data, distinguishing flaws more precisely and achieving a better signal-to-noise ratio (SNR) [32]. Later, a state-of-the-art survey of NDE data fusion techniques was provided by [PERSON] et al. [33]. Each individual non-destructive method has its own advantages and limitations. For example, ultrasonic waves are best for the detection of delamination or disbond type of defects, while radiography is best for the detection of changes in density. Therefore, the most suitable single NDT method has to be selected according to the requirements to achieve the evaluation goal. Furthermore, it is observed that none of the single NDE techniques can fully characterize the object inspected, and it has its own uncertainty of the obtained result. Moreover, accuracy and reliability requirements are constantly increasing, especially in the field of aerospace and nuclear power industries. Therefore, to meet such requirements, multiple inspection techniques are needed. The fusion of multiple inspections benefits from the advantages of all individual non-destructive inspection methods and diminishes the uncertainty of the measurement result, as well as enhancing the signal.