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Architecture for Fault Detection in Sandwich Panel Production Using Visual Analytics

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Hybrid Artificial Intelligent Systems (HAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13469))

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Abstract

Technology can give industries the ability to create products/materials/services that meet customer needs and comply with applicable regulatory obligations. In this context, an automatic damage detection system is proposed for sandwich panels. Instead of relying on manual inspection, the system is based on artificial vision and operates with high accuracy in an industrial environment, ensuring traceability in product quality, reducing the percentage of returns caused by imperfections. The adaptive thresholding method seeks to identify the pixel intensities found on the surface of the sandwich panel. Unlike existing methods, the proposed algorithm is based on an adaptive threshold that uses the local characteristics of an image to segment and classify damage on the surfaces of sandwich panels, seeking to reject or accept a product according to the quality levels defined by the standard. The experimental results propose to generate a comparison with a sandwich panel damage detection method based on a convolutional neural network. The results of the experiment show that the proposed thresholding-based method has better accuracy and F1Score than deep learning methods. Moreover, this system is able to improve the industrial standards of sandwich panel manufacturing according to the standard, which limits the allowable imperfections, pointing out only the maximum admissible value of manufacturing imperfections to obtain a quality product.

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Acknowledgements

This research has been supported by the project “Intelligent and sustainable mobility supported by multi-agent systems and edge computing (InEDGEMobility): Towards Sustainable Intelligent Mobility: Blockchain-based framework for IoT Security”, Reference: RTI2018-095390-B-C32, financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), the State Research Agency (AEI) and the European Regional Development Fund (FEDER).

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Authors and Affiliations

  1. Grupo de Investigacin BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Calle Espejo 2, 24.2, 37007, Salamanca, Spain

    Sebastian Lopez Florez, Marcos Severt Silva, Alfonso González-Briones & Pablo Chamoso

  2. Universidad Tecnológica de Pereira, Cra. 27 N 10-02, Pereira, Risaralda, Colombia

    Sebastian Lopez Florez

Authors
  1. Sebastian Lopez Florez
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  2. Marcos Severt Silva
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  3. Alfonso González-Briones
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  4. Pablo Chamoso
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Corresponding author

Correspondence to Sebastian Lopez Florez .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  4. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Analytics Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. Department of Computer Science, University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Applied Computational Intelligence, University of Burgos, Burgos, Burgos, Spain

    Álvaro Herrero

  8. Universidad Pablo de Olavide, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Florez, S.L., Silva, M.S., González-Briones, A., Chamoso, P. (2022). Architecture for Fault Detection in Sandwich Panel Production Using Visual Analytics. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2022. Lecture Notes in Computer Science(), vol 13469. Springer, Cham. https://doi.org/10.1007/978-3-031-15471-3_25

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  • DOI: https://doi.org/10.1007/978-3-031-15471-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15470-6

  • Online ISBN: 978-3-031-15471-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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