Adaptive approach for filtering the sigma phase in austenitic stainless steel metallographic microstructures

This paper presents an adaptive approach, based on image processing and use of self-organizing maps for filtering, analyzing, and determining the sigma phase percentage in metallographic images of austenitic stainless steel. In order to predict the remaining life of the austenitic stainless steel (12X18H12T), a metallographic analysis of the sigma phase percentage should be made. Following steel microstructure preparation, a series of microscopic digital images are used to measure this parameter. The digital images contain low amount of Gaussian noise and the sigma phase particles must be separated from all non-metal and other small-size or noise inclusions. Implementation of automated measurement leads to more accurate results and minimizes the subjective evaluation factors. A set of morphological features for each blob in a test group of blobs is analyzed using Kohonen self-organizing neural network after applying image filtering and blob detection algorithm. Self-organizing maps are used to filter the blobs. The achieved results are compared with those, obtained from the application of other metallographic methods for the same purpose.