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Article

Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network

1
Doctorate Program in Industrial Technologies, International School of Doctorate, Technical University of Cartagena, 30202 Cartagena, Spain
2
Innovation Division, MTorres Diseños Industriales SAU, Ctra. El Estrecho-Lobosillo, Km 2, Fuente Álamo, 30320 Murcia, Spain
3
Department of Structures, Construction and Graphical Expression, Technical University of Cartagena, 30202 Cartagena, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(19), 6856; https://doi.org/10.3390/app10196856
Received: 25 August 2020 / Revised: 13 September 2020 / Accepted: 27 September 2020 / Published: 29 September 2020
(This article belongs to the Special Issue New Trends in Design Engineering)
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. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility. View Full-Text
Keywords: advanced manufacturing; Industry 4.0; product development; product design; design for X methods; tolerancing advanced manufacturing; Industry 4.0; product development; product design; design for X methods; tolerancing
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MDPI and ACS Style

Ruiz, 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

AMA Style

Ruiz L, Torres M, Gómez A, Díaz S, González JM, Cavas F. Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network. Applied Sciences. 2020; 10(19):6856. https://doi.org/10.3390/app10196856

Chicago/Turabian Style

Ruiz, Leandro, Manuel Torres, Alejandro Gómez, Sebastián Díaz, José M. González, and Francisco Cavas. 2020. "Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network" Applied Sciences 10, no. 19: 6856. https://doi.org/10.3390/app10196856

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