A new improved Laws-based descriptor for surface roughness evaluation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-03

AUTHORS

Enrique Alegre, Joaquín Barreiro, Sir Alexci Suárez-Castrillón

ABSTRACT

A new descriptor that allows to classify turned metallic parts based on their superficial roughness is proposed in this paper. The material used for the tests was AISI 6150 steel, regarded as one of the reference steels in the market. The proposed solution is based on a vision system that calculates the actual roughness by analysing texture on images of machined parts. A new developed R5SR5S kernel for quantifying roughness is based on the R5R5 mask presented by Laws. Results from computing standard deviation from images obtained with the proposed R5SR5S kernel allow us to classify the images with a hit rate of 95.87% using linear discriminant analysis and 97.30% using quadratic discriminant analysis. These results show that the proposed technique can be effectively used to evaluate roughness in machining processes. More... »

PAGES

605-615

References to SciGraph publications

  • 2009-01. Evaluation of three-dimensional surface roughness parameters based on digital image processing in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2005-06. The effects of machining process variables and tooling characterisation on the surface generation in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00170-011-3507-z

    DOI

    http://dx.doi.org/10.1007/s00170-011-3507-z

    DIMENSIONS

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