Earticle

초록

영어

The morphology and the distribution of graphite grains are the decisive factors in judging the properties of the material cast iron. There are six classes of graphite grain morphology defined by ISO-945 through reference drawings for cast iron graphite grain classification. These reference drawings are universally accepted for classification of graphite grains. Many shape representations and retrieval methods exist. Among them, methods based on Fourier descriptors (FD) achieve acceptable results in classification compared to other methods. Different shape signatures have been exploited to derive FDs, however, FDs derived from different signatures can have significant effect on the result of classification [17]. In this paper, a performance analysis of classification of graphite grains using spectral and spatial features is performed. The neural network classifier based on radial basis function has been employed for classification. The experimentation is carried out using the metallographic images from the well known microstructures library [6]. For training and testing the network, the grain shapes identified in ISO-945 reference drawings and the grain classification by the experts are used. The FDs derived from centroid distance function and neural network classifier with radial basis function yield better classification results for graphite grains.

목차

Abstract
 1. Introduction
  1.1 Classes of Grains in Cast Iron
  1.2 Materials and Methods
 2. Proposed Method
  2.1 Preprocessing
 3. Shape Signatures
  3.1 Complex Coordinates
  3.2 Centroid Distance
  3.3 Curvature Signature
  3.4 Cumulative Angular Function
 4. Discrete Fourier Descriptors
 5. Experimental Results and Discussion
 6. Conclusion
 References

키워드

저자

• Pattan Prakash [ Dept. of Computer Science and Engineering, York University ]
• V. D. Mytri [ GND College of Engineering ]
• P. S. Hiremath [ Dept. of Computer Science, Gulbarga University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605