Texture Descriptors for Automatic Estimation of Workpiece Quality in Milling

• Manuel Castejón-Limas ^[1] ; Lidia Sánchez-González ^[1] ; Javier Díez-González ^[1] ; Laura Fernández-Robles ^[1] ; Virginia Riego ^[1] ; Hilde Pérez ^[1]
  1. [1] Universidad de León

     Universidad de León

     León, España

     
• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 734-744
• Idioma: inglés
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• 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.