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Hybrid Distributed Cascade Convolutional Neural Networks Model for Riveting Processes

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Proceedings of the Sixteenth International Conference on Management Science and Engineering Management – Volume 1 (ICMSEM 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 144))

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Abstract

The field of artificial intelligence is experiencing a great interest in manufacturing companies for the inspection of parts and verification of images. The current trend in the sector is to implement these novel methodologies in industrial environments so that they can benefit from their advantages over traditional systems. Quality and production managers are increasingly interested in replacing the classic inspection methods with this new approach due to its flexibility and precision. Traditional methods have some weaknesses when it comes to inspecting parts, such as sensitivity to disturbances. In an industrial environment these disturbances can be changes in lighting during the day or during the year, the appearance of external elements such as dust or dirt. The use of new convolutional neural network techniques allows training including disturbance scenarios, teaching the artificial neural network to detect non-verse defects influenced by changes in light or by the appearance of dust. In this way, it is possible to drastically reduce false positives, avoiding costly stops in production and maximizing the precision of detection and classification of each defect. This work studies the implementation of a hybrid model based on a cascade detection neural network with a classification neural network in an industrial environment.

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Acknowledgements

The work reported herewith has been financially by the Direccin General de Universidades, Investigacin e Innovacin of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102).

Author information

Authors and Affiliations

  1. HCTLab Research Group, Electronics and Communications Technology Department, Universidad Autonoma de Madrid, 28049, Madrid, Spain

    Diego Ortega Sanz & Carlos Quiterio Gómez Muñoz

  2. Ingenium Research Group, University of Castilla-La Mancha, Ciudad Real, Spain

    Fausto Pedro García Márquez

Authors
  1. Diego Ortega Sanz
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  2. Carlos Quiterio Gómez Muñoz
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  3. Fausto Pedro García Márquez
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Corresponding author

Correspondence to Carlos Quiterio Gómez Muñoz .

Editor information

Editors and Affiliations

  1. Business School, Sichuan University, Chengdu, Sichuan, China

    Jiuping Xu

  2. Department of Industrial Engineering, Gazi University, Faculty of Engineering, Ankara, Turkey

    Fulya Altiparmak

  3. School of Mathematical Sciences, University of Khartoum, Khartoum, Sudan

    Mohamed Hag Ali Hassan

  4. University of Castilla-La Mancha (UCLM), Ciudad Real, Spain

    Fausto Pedro García Márquez

  5. Institute of Control Systems, Azerbaijan National Academy of Sciences, Baku, Azerbaijan

    Asaf Hajiyev

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Sanz, D.O., Muñoz, C.Q.G., Márquez, F.P.G. (2022). Hybrid Distributed Cascade Convolutional Neural Networks Model for Riveting Processes. In: Xu, J., Altiparmak, F., Hassan, M.H.A., García Márquez, F.P., Hajiyev, A. (eds) Proceedings of the Sixteenth International Conference on Management Science and Engineering Management – Volume 1. ICMSEM 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-031-10388-9_29

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