Computer Vision and Classification Techniques on the Surface Finish Control in Machining Processes View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2008

AUTHORS

Enrique Alegre , Joaquín Barreiro , Manuel Castejón , Suarez

ABSTRACT

This work presents a method to perform a surface finish control using a computer vision system. Test parts used were made of AISI 303 stainless steel and were machined with a MUPEM CNC multi-turret parallel lathe. Using a Pulnix PE2015 B/W camera, a diffuse illumination and a industrial zoom, 140 images were acquired. We have applied a vertical Prewitt filter to all the images obtaining two sets, the original one and the filtered. We have described the images using three different methods. The first features vector was composed by the mean, standard deviation, skewness and kurtosis of the image histogram. The second features vector was made up by four Haralick descriptors – contrast, correlation, energy and homogeneity. The last one was composed by 9 Laws descriptors. Using k-nn we have obtained a hit rate around 90 % with filtered images and, the best one, using Laws features vector of 92.14% with unfiltered images. These results show that it is feasible to use texture descriptors to evaluate the rugosity of metallic parts in the context of product quality inspection. More... »

PAGES

1101-1110

References to SciGraph publications

  • 1981. Image Texture Analysis Techniques - A Survey in DIGITAL IMAGE PROCESSING
  • 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

    Image Analysis and Recognition

    ISBN

    978-3-540-69811-1
    978-3-540-69812-8

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-540-69812-8_110

    DOI

    http://dx.doi.org/10.1007/978-3-540-69812-8_110

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1028389310


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