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Fuzzy Rule Based Classification and Quantification of Graphite Inclusions from Microstructure Images of Cast Iron

Published online by Cambridge University Press:  07 November 2011

Pattan Prakash*
Affiliation:
Department of Computer Science and Engineering, PDA College of Engineering, Gulbarga-585102 (Karnataka State), India
V.D. Mytri
Affiliation:
GND Collegeof Engineering, Bidar-585403 (Karnataka State), India
P.S. Hiremath
Affiliation:
Department of Computer Science, Gulbarga University, Gulbarga 585106 (Karnataka State), India
*
Corresponding author. E-mail: prakashpattan@gmail.com
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Abstract

The quantification of three classes of graphite inclusions in cast iron, namely, nodular, flake, and irregular, is the most important process in the foundry industry. This classification is based on the ISO 945 proposed morphology of graphite inclusions. This work presents a novel solution for automatic quantitative analysis of graphite inclusions into the three mentioned classes. The proposed work comprises three stages, namely, preprocessing of micrographs, classification of graphite inclusions, and then quantification of inclusions in each class. An effort has been made in this work to propose a minimum set of features to represent graphite inclusion morphology. The method employs just two geometric shape descriptors: the diameter ratio and the area ratio. A fuzzy rule based classifier is built using known feature values that are efficient in the classification of the three classes of graphite inclusions. The proposed method is automatic, fast, and provides the basis for determining many more morphological parameters that can be determined with the least effort. The results obtained by the proposed method are compared with the manual method. It is observed that the results obtained from the proposed method are useful in the optimization of cast iron manufacturing in the foundry industry.

Type
Software and Techniques Development
Copyright
Copyright © Microscopy Society of America 2011

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References

REFERENCES

Abbasi, S. & Mokhtarian, F. (1999). Robustness of shape similarity retrieval under affine transformation, in challenge of image retrieval. In Proceedings of the Ninth ACM International Conference on Multimedia, pp. 4150. New York: ACM.Google Scholar
Arnould, X., Coster, M., Chermant, J.L., Chermant, L., Chartier, T. & El Moataz, A. (2001). Segmentation and grain size of ceramic. Image Anal Stereol 20, 131135.CrossRefGoogle Scholar
ASM International Handbook Committee (2004). ASM Handbook, Vol. 9, Metallography and Microstructures. Metals Park, OH: ASM International.Google Scholar
Benesova, W., Rinnhofer, A. & Gerhard, J. (2006). Determining the average grain size of super-alloy micrographs. In Proceedings of IEEE International Conference on Image Processing (ICIP 2006), Atlanta, GA, pp. 27492752. New York: IEEE.Google Scholar
Bhoyar, K.K. & Kakde, O.G. (2005). A neural network approach to JNS color histogram and its application to color image retrieval. In Proceedings of International Conference on Cognition and Recognition (ICCR), Mandya, India.Google Scholar
Chen, Y., Zhang, M., Lu, P. & Wang, Y. (2005). Local moment invariant analysis. In IEEE Proceedings of the Computer Graphics, Imaging and Vision: New Trends (CGIV'05), Beijing, China, pp. 137140. New York: IEEE.Google Scholar
Jain, A.K. (1989). Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Jamil, N., Abu Baker, Z. & Sembok, T.M.T. (2006). Image retrieval of songket motifs using simple shape descriptors. In Proceedings of the International Conference on Geometric Modeling and Imaging—New Trends 2006 (GMIA '06), pp. 171176. New York: IEEE.Google Scholar
Keller, J.M., Qiu, H. & Tihani, H. (1996). Fuzzy logic rules in low and mid level computer vision tasks. In Proceedings of NAFIP'96, Berkeley, CA, pp. 324338.Google Scholar
Kulkarni, S., Verma, B., Sharma, P. & Selvaraj, H. (1999). Content based image retrieval using a neuro-fuzzy technique. In Proceedings of IEEE International Joint Conference on Neural Networks, Washington, DC, pp. 846850. New York: IEEE.Google Scholar
Li, Q., Shi, Z. & Luo, S. (2007). Image retrieval based on fuzzy color semantics. In Proceedings of the IEEE Conference on Fuzzy Systems, London, UK, pp. 15.Google Scholar
Mamdani, E.H. & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Machine Studies 7, 113.CrossRefGoogle Scholar
Mehta, D., Diwakar, E.S.V.N.L. & Jawahar, C.V. (2003). A rule-based approach to image retrieval. In Convergent Technologies (IEEE TENCON), Bangalore, India, pp. 586590.Google Scholar
Pattan, P., Mytri, V.D. & Hiremath, P.S. (2009). Classification of cast iron based on graphite grain morphology using simple shape descriptor. Eng Technol 2(4), 3842.Google Scholar
Pattan, P., Mytri, V.D. & Hiremath, P.S. (2010a). Classification of cast iron based on graphite grain morphology using neural network approach. SPIE 7546, 75462S-175462S-6.Google Scholar
Pattan, P., Mytri, V.D. & Hiremath, P.S. (2010b). An improved algorithm for classification of graphite grains in cast iron microstructure images using geometric shape features. International Conference on ThinkQuest 2010: Contours of Computing Technologies, Sr. No. 34, pp. 215–221. Springer.Google Scholar
Persoon, E. & Fu, K.-S. (1977). Shape discrimination using Fourier descriptors. IEEE Trans Syst Man Cybernetic 7(3), 170179.CrossRefGoogle Scholar
Russ, J.C. (2007). The Image Processing Handbook, 5th ed.Boca Raton, FL: CRC Press.Google Scholar
Sarfraz, M. & Ridha, A. (2007). Content-based image retrieval using multiple shape descriptors. Computer Systems and Applications-AICCSA'07, Amman, Jordan pp. 730737. Hanover, Germany: IEEE Computer Society Press.Google Scholar
Sonka, M., Hlavac, V. & Boyle, R. (1999). Image Processing, Analysis, and Machine Vision, 2nd ed.India: PWS Publishing.Google Scholar
Wojnar, L. (1999). Image Analysis, Applications in Materials Engineering. Boca Raton, FL: CRC Press.Google Scholar