Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal

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Author(s):	Albuquerque, Victor H. C. ; Nakamura, Rodrigo Y. M. ; Papa, Joao P. ; Silva, Cleiton C. ; Tavares, Joao Manuel R. S. ; Tavares, JMRS ; Jorge, RMN Total Authors: 7
Document type:	Journal article
Source:	COMPUTATIONAL VISION AND MEDICAL IMAGE PROCESSING: VIPIMAGE 2011; v. N/A, p. 6-pg., 2012-01-01.
Abstract
Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that. 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of. 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. (AU)

FAPESP's process:	09/16206-1 - New trends on optimum-path forest-based pattern recognition
Grantee:	João Paulo Papa
Support Opportunities:	Research Grants - Young Investigators Grants