Surface Finish Control in Machining Processes Using Haralick Descriptors and Neuronal Networks View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Enrique Alegre , Rocío Alaiz-Rodríguez , Joaquín Barreiro , Eduardo Fidalgo , Laura Fernández

ABSTRACT

This paper presents a method to perform a surface finish control using a computer vision system. The goal pursued was to design an acceptance criterion for the control of surface roughness of steel parts, dividing them in those with low roughness - acceptable class - and those with high roughness - defective class. We have used 143 images obtained from AISI 303 stainless steel machining. Images were described using three different methods - texture local filters, the first four Haralick descriptors from the gray-level co-occurrence matrix and a 20 features vector obtained from the first subband of a wavelet transform of the original image and also the gray-level original image. Classification was conducted using K-nn and Neuronal Networks. The best error rate - 4.0% - with k-nn was achieved using texture descriptors. With the neuronal network, an eight node hidden layer network using Haralick descriptors leads to the optimal configuration - 0.0% error rate. More... »

PAGES

231-241

References to SciGraph publications

  • 2003-11. On-line surface roughness recognition system using artificial neural networks system in turning operations in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2002-02. A Study of Computer Vision for Measuring Surface Roughness in the Turning Process in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • Book

    TITLE

    Computational Modeling of Objects Represented in Images

    ISBN

    978-3-642-12711-3
    978-3-642-12712-0

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-12712-0_21

    DOI

    http://dx.doi.org/10.1007/978-3-642-12712-0_21

    DIMENSIONS

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