Detection and classification of aircraft fixation elements during manufacturing processes using a convolutional neural network
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AuthorRuiz Lozano, Leandro; Torres, Manuel; Gómez Vilanova, Alejandro; Díaz Carrillo, Sebastián; González Sesma, José Manuel; [et al.]
Knowledge AreaExpresión Gráfica en Ingeniería
SponsorsThis publication was carried out as part of the project Nuevas Uniones de estructuras aeronáuticas reference number IDI-20180754. This project has been supported by the Spanish Ministry of Ciencia e Innovación and Centre for Industrial Technological Development (CDTI).
Bibliographic CitationRuiz, L.; Torres, M.; Gómez, A.; Díaz, S.; González, J.M.; Cavas, F. Detection and classification of aircraft fixation elements during manufacturing processes using a convolutional neural network. Appl. Sci. 2020, 10, 6856. https://doi.org/10.3390/app10196856
Design for X methods
The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. ...
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