## 1 Introduction  
Every day, the aerospace industry works on developing products with high added value that are subjected to the highest sustainability and efficiency demands in an increasingly demanding and competitive market [1; 2; 3; 4]. During the transition toward Industry 4.0, digital technologies like artificial vision, augmented or virtual reality, big data and cybersecurity have aroused much interest in aeronautical manufacturing processes [4; 5; 6; 7; 8]. In the specific case of manufacturing and assembly lines for large structures, manufacturers aim to automate the entire process through adaptive robotic systems [9; 10] which, together with vision and artificial intelligence systems, will make it possible to improve the plant's efficiency and flexibility, and to reduce the associated cost per unit of aeronautical structures. One fundamental part of referencing these processes is the detection and measurement of the parts in their real positions by artificial vision systems, which have been used in different industrial sectors [6; 11; 12; 13; 14; 15]. Non-contact methods are being increasingly employed during aeronautical manufacturing processes as enabling technology in the fields of verification [6], metrology [7; 10; 13] and quality analysis [11] are implemented, both for their excellent inherent performance and the saved cost of accelerating production rates to meet production demands. However, their flexibility and reconfiguration must balance with accuracy and reliability to find suitable applications. Therefore, their use still remains a challenge for the aerospace industry. In addition, another handicap of non-contact vision systems is that they work correctly under optimal and controlled operating conditions, but their performance decreases in the following scenarios: industrial environments with manufacturing and assembly processes, because of the diversity of objects used (drills, rivets, countersinks, etc.) and the acquisition of images in adverse environments, due to the presence of dirt (chips, oil drops, etc.) or incorrect lighting (shadows, changes in lighting, etc.). These problems entail image processing that can become computationally expensive, especially given the resolution required by images to obtain measurements with the best possible precision [13]. On the manufacturing and assembly lines of aircraft structures, non-contact vision systems are housed in the heads of machines and play a critical role in correct robot operation during inspections for roto-translation [16], inspection for redrilling [17] and for verifying inspections [18]. Our research used images of the vision systems of these three robot types. Images obtained by vision systems can be identified and classified by disruptive technology, such as artificial intelligence [14; 19; 20; 21], and machine-learning techniques specifically have been used widely in the industry to classify objects [6; 7; 8; 13]. However, machine-learning systems are based on two different stages, in which users have to define the characteristics to be extracted. Thus, optimal network performance could be limited by the form of selection for characteristic vectors [22]. To overcome this difficulty, our study used convolutional neural networks (CNNs). Unlike conventional machine-learning systems, CNNs can perform feature extraction and recognition at the same time in a single network and, therefore, optimize the classifier's performance [23]. CNNs have been applied to different sectors [24; 25] and, although they have presented favorable results in the classification field, very few studies have been carried out in industrial applications for surface inspections [20] or for detecting defects [26]. To the best of the authors' knowledge, artificial vision systems with CNNs have not been used to design or validate a classifier for aeronautical fastening elements on a manufacturing and assembly line of large parts with structural responsibility.