Texture Descriptors for Automatic Estimation of Workpiece Quality in Milling

  1. Manuel Castejón-Limas 1
  2. Lidia Sánchez-González 1
  3. Javier Díez-González 1
  4. Laura Fernández-Robles 1
  5. Virginia Riego 1
  6. Hilde Pérez 1
  1. 1 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Libro:
Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings
  1. Hilde Pérez García (coord.)
  2. Lidia Sánchez González (coord.)
  3. Manuel Castejón Limas (coord.)
  4. Héctor Quintián Pardo (coord.)
  5. Emilio Corchado Rodríguez (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-29859-3,  978-3-030-29858-6

Año de publicación: 2019

Páginas: 734-744

Congreso: Hybrid Artificial Intelligent Systems (14. 2019. León)

Tipo: Aportación congreso

DIALNET GOOGLE SCHOLAR

Resumen

Milling workpiece present a regular pattern when they are correctly machined. However, if some problems occur, the pattern is not so homogeneous and, consequently, its quality is reduced. This paper proposes a method based on the use of texture descriptors in order to detect workpiece wear in milling automatically. Images are captured by using a boroscope connected to a camera and the whole inner surface of the workpiece is analysed. Then texture features are computed from the coocurrence for each image. Next, feature vectors are classified by 4 different approaches, Decision Trees, K Neighbors, Na¨ıve Bayes and a Multilayer Perceptron. Linear discriminant analysis reduces the number of features from 6 to 2 without loosing accuracy. A hit rate of 91.8% is achieved with Decision Trees what fulfils the industrial requirements.

Fuente de los datos: Dialnet