Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-24T07:33:39.606Z Has data issue: false hasContentIssue false
  • English
  • Français

Automatic Classification of Graphite in Cast Iron

Published online by Cambridge University Press:  07 July 2005

Otávio da F.M. Gomes  and
Sidnei Paciornik
Otávio da F.M. Gomes
Affiliation:
Department of Materials Science and Metallurgy, Catholic University of Rio de Janeiro, Rua Marquês de S. Vicente, 225, sala 501L, Gávea, Rio de Janeiro, RJ, 22453-900, Brazil CETEM—Centre for Mineral Technology, Av Ipê, 900, Ilha do Fundão, Rio de Janeiro, RJ, 21941-590, Brazil
Sidnei Paciornik
Affiliation:
Department of Materials Science and Metallurgy, Catholic University of Rio de Janeiro, Rua Marquês de S. Vicente, 225, sala 501L, Gávea, Rio de Janeiro, RJ, 22453-900, Brazil
Get access

Abstract

A method for automatic classification of the shape of graphite particles in cast iron is proposed. In a typical supervised classification procedure, the standard charts from the ISO-945 standard are used as a training and validation population. Several shape and size parameters are described and used as discriminants. A new parameter, the average internal angle, is proposed and is shown to be relevant for accurate classification. The ideal parameter sets are determined, leading to validation success rates above 90%. The classifier is then applied to real cast iron samples and provides results that are consistent with visual examination. The method provides classification results per particle, different from the traditional per field chart comparison methods. The full procedure can run automatically without user interference.

Keywords

cast ironautomatic classificationISO-945shape factorimage analysis
Type
TECHNIQUES FOR MICROSCOPY AND MICROANALYSIS
Information
Microscopy and Microanalysis , Volume 11 , Issue 4 , August 2005 , pp. 363 - 371
Copyright
© 2005 Microscopy Society of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Callister, W.D. (2003). Materiais Science and Engineering: An Introduction. New York: John Wiley and Sons.
Chen, C. (1973). Statistical Pattern Recognition. Rochelle Park, NJ: Hayden Book Co.
Duda, R.O. & Hart, P.E. (1973). Pattern Classification and Scene Analysis. New York: John Wiley & Sons.
Fargues, J. & Stucky, M. (1994). Caractérisation de la forme du graphite à l'aide d'un analyseur d'images. Rev Métall 91, 267–275.Google Scholar
Gomes, O.F.M. (2001). Processamento e Análise de Imagens Aplicada à Caracterização Automática de Materiais. M.Sc. dissertation. Rio de Janeiro, Brazil: Pontifícia Universidade Católica do Rio de Janeiro.
Gonzalez, R.C. & Woods, R.E. (1992). Digital Image Processing. Reading, MA: Addison-Wesley.
Grum, J. & Sturm, R. (1995). Computer supported recognition of graphite particle forms in cast iron. Acta Stereologica 14, 91–96.Google Scholar
Hecht, M. (1995). Morphologie du graphite et caracteristiques mecaniques des fontes a graphite spheroidal. Fonderie, fondeur d'aujourd'hui 147.
ISO (1975). Cast iron—Designation of microstructure of graphite. ISO 945:1975, International Standards Organization.
Livens, S. (1998). Image Analysis for Material Characterization. Thesis. Antwerpen, Belgium: Universitaire Instelling Antwerpen.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9, 62–66.Google Scholar
Russ, J.C. (1995). The Image Processing Handbook. Boca Raton, FL: CRC Press.
Stefanescu, D. M., Hummer, R., & Nechtelberger, E. (1988). Compacted graphite irons. In Metals Handbook, 9th ed., vol. 15, pp. 667–677. Materials Park, OH: ASM International.
Toussaint, G.T. (1974). Bibliography on estimation of misclassification. IEEE Trans Inform Theor 20, 472–479.Google Scholar
Van Vlack, L.H. (1964). Elements of Materials Science. Reading, MA: Addison-Wesley.
Vieira, P.R.M. & Paciornik, S. (2001). Uncertainty evaluation of metallographic measurements by mage analysis and thermodynamic modeling. Mater Char 47, 219–226.Google Scholar
Weeks, A.R., Jr. (1996). Fundamentals of Electronic Image Processing. Bellingham, WA: SPIE Optical Engineering Press.
Wojnar, L. (2000). Analysis and interpretation. In Practical Guide to Image Analysis, pp. 145–202. Materials Park, OH: ASM International.