Classification of cast iron based on graphite grain morphology using neural network approach

Abstract

The ISO-945 committee has defined six classes of grain morphology through reference drawings for cast iron graphite grain classification. These reference drawings are universally accepted for classification of graphite grains. The main aim of this work is to propose a neural network approach for cast iron classification based on graphite grain morphology by processing microstructure images. The two sets of shape features investigated are, Simple Shape Descriptors (SSDs) and Moment Invariants(MIs). The classifiers like, feed forward neural network with back propagation and radial basis functions are also investigated. The experimentation is carried out using the metallographic images from the well known microstructures library. For training and testing the networks, the grain shapes identified in ISO-945 reference drawings and the grain classification by the experts are used. The moment invariant shape features and neural network classifier with radial basis function yield better classification results for graphite grains. © 2010 Copyright SPIE - The International Society for Optical Engineering.