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Improvement of Fixation Elements Detection in Aircraft Manufacturing

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Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence (IWINAC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13259))

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Abstract

The requirements for accuracy and reliability in the manufacturing processes of large aircraft structures are among the most demanding in the industry due to the continuous development of advanced manufacturing processes with tight tolerances and high requirements for process integrity. The main technique for the operation of many of these processes is the detection and precise measurement of fasteners by artificial vision systems in real time, however these systems require adjustment of multiple parameters and do not work correctly in uncontrolled scenarios, which require intervention operations such as manual supervision of the measurement process, leading into a reduction in the autonomy of automated systems.

In this study, a new Deep Learning algorithm based on a Single Shot Detector neural network is proposed for the detection and measurement drills and other fixation elements (such as rivets and temporary fasteners) in an uncontrolled industrial manufacturing environment. The convergence of the new network has been optimized for the detection of elements of a circular nature, instead of the generic anchor boxes usually used. In addition, a fine-tuning algorithm based on a new characterization parameter of the circular geometry is applied to the results obtained from the network. This new metric has made it possible to define a quality parameter with respect to the measurement made.

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Funding

This publication has been carried out in the framework 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).

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Authors and Affiliations

  1. Doctorate Program in Industrial Technologies, International School of Doctorate, Technical University of Cartagena, 30202, Cartagena, Spain

    Leandro Ruiz

  2. Innovation Division, MTorres Diseños Industriales SAU, Ctra. El Estrecho-Lobosillo, Km 2, Fuente Álamo, 30320, Murcia, Spain

    Leandro Ruiz, Sebastián Díaz & José M. González

  3. Structures, Construction and Graphical Expression, Technical University of Cartagena, 30202, Cartagena, Spain

    Francisco Cavas

Authors
  1. Leandro Ruiz
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  2. Sebastián Díaz
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  3. José M. González
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  4. Francisco Cavas
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Corresponding author

Correspondence to Francisco Cavas .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Universidad Nacional de Educación a Distancia, Madrid, Spain

    Félix de la Paz López

  4. Ohio State University, Columbus, OH, USA

    Hojjat Adeli

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Ruiz, L., Díaz, S., González, J.M., Cavas, F. (2022). Improvement of Fixation Elements Detection in Aircraft Manufacturing. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. IWINAC 2022. Lecture Notes in Computer Science, vol 13259. Springer, Cham. https://doi.org/10.1007/978-3-031-06527-9_37

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  • DOI: https://doi.org/10.1007/978-3-031-06527-9_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06526-2

  • Online ISBN: 978-3-031-06527-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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