## 1 Introduction
[19] developed 1D-YOLO, a network based on the use of dilated convolutions, the Recursive Feature Pyramid [20] and the R-CNN [21] to detect damage on aircraft composite materials using a fusion of C-scan and ultrasonic A-scan data with an accuracy of 94.5% and a mean average precision of 80%. This combination of signal and image detection and classification has potential as a method for achieving production-level defect ultrasonic detection applied to composites. However, current ultrasonic techniques do not provide enough information about some of the most common manufacturing defects of composite materials, e.g., porosity, 3-D maps of fiber volume fraction, out-of-plane wrinkling, in-plane fiber waviness, pyl-drop, ply-overlap location, and ply stacking sequence, which are not fully detectable or measurable [22]. In these cases, a different NDT technique would be required to label data and develop datasets for supervised learning tasks. One option is the use of X-ray computed tomography (XCT) because it enables the 3D reconstruction of the internal microstructure of a sample. It has been widely used to perform quantitative analysis on metal [23; 24] and composite materials [25; 26]. However, its use in in-service environments has a number of limitations. For example, there needs to be access to the item under inspection from all angles (360\({}^{\circ}\)), the use of ionizing radiation generates safety concerns, the equipment is costly, and inspection times can be protracted. In addition, X-ray images tend to have low brightness; a possible solution could be found by improving visual effects [27; 28]. On the other hand, XCT is one of the few techniques that provides a full 3D picture of the microstructure of the inspected item with sufficient spatial resolution (1000 times higher than UT). Thus, XCT and UT data fusion is an opportunity: automatic defect detection algorithms could be developed for ultrasonic data using the XCT data as ground truth. One example is the work of [PERSON] et al. [29] on the improvement of delamination characterization. Accordingly, labeling is possible in the cases where current UT techniques are unable to detect defects, or fail to provide enough information to this end or in the presence of noisier data. Furthermore, detection models may be developed from any component, and not only from demo components with known inserted defects. In other words, XCT and ultrasounds fusion enables the development of automatic detection/segmentation in ultrasonic data for cases where UT alone is not enough to perform labeling. As mentioned above, porosity is one type of defect for which UT techniques do not provide enough information. It is also one of the most common manufacturing defects of composite materials. The appearance of voids is random, and there is currently no manufacturing method that is consistently free from porosity. Usually, a threshold of 2% void volume fraction is set as acceptance criteria. However, the estimation of void volume fraction using UT is highly dependent on material, manufacturing process, and inspection equipment [30]. The void size, shape, and distribution have an impact on the estimation of the void volume fraction [31; 32], and on the mechanical properties [33]. [PERSON] et al. examine the performance of ultrasonic array imaging in distinguishing non-sharp defects, assuming that all defects are crack-like. However, they state that such an approach could be over-conservative and lead to pessimistic assessments of structural components [34]. Therefore, current UT methods are capable of detecting porosity, but do not provide relevant information about its quantity, shape, and size [30; 31; 32]. In addition, it is challenging to detect a flaw near another flaw or structure because the resulting waves usually overlap. In composite materials, the main impact of this effect is on close-proximity flaws and at the edges of the part under inspection [35]. In this article, we develop a methodology for XCT and ultrasonic data fusion that trains a CNN to segment porosity.