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Local Binary Pattern Features to Detect Anomalies in Machined Workpiece

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Hybrid Artificial Intelligent Systems (HAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12344))

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Abstract

Quality standards involve objective procedures that guarantee the criteria keep constant during the process. In manufacturing, an important task that operators do by visual inspection is the evaluation of the surface finish of a machined workpiece. In this paper, a vision-based system that represents the image texture by a Local Binary Pattern vector is proposed. As the machined parts that present a regular pattern correspond with no wear surfaces, texture descriptors give such information making possible to determine automatically the presence of wear along the workpiece surface. Four different classification techniques are considered so as to determine the best approach. Among them, Random Forest classification algorithm yields the best hit rate with a 86.0%. Such results satisfies the expert demands.

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Authors and Affiliations

  1. Departamento de Ingenierías Mecánica, Informática y Aeroespacial, Universidad de León, 24071, León, Spain

    Lidia Sánchez-González, Virginia Riego, Manuel Castejón-Limas & Laura Fernández-Robles

Authors
  1. Lidia Sánchez-González
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  2. Virginia Riego
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  3. Manuel Castejón-Limas
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  4. Laura Fernández-Robles
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Corresponding author

Correspondence to Lidia Sánchez-González .

Editor information

Editors and Affiliations

  1. University of Oviedo, Oviedo, Spain

    Enrique Antonio de la Cal

  2. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  3. University of A Coruña, Ferrol, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Sánchez-González, L., Riego, V., Castejón-Limas, M., Fernández-Robles, L. (2020). Local Binary Pattern Features to Detect Anomalies in Machined Workpiece. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_55

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  • DOI: https://doi.org/10.1007/978-3-030-61705-9_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61704-2

  • Online ISBN: 978-3-030-61705-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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