Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal
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Data
2012-02-13
Autores
Albuquerque, Victor H. C.
Nakamura, Rodrigo Y. M. [UNESP]
Papa, João Paulo [UNESP]
Silva, Cleiton C.
Tavares, João Manuel R. S.
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Resumo
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 γ 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 γ 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. © 2012 Taylor & Francis Group.
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Automatic segmentations, Bayesian classifier, Chemical compositions, Machine learning techniques, Pattern recognition techniques, Recognition rates, Steel quality, Superduplex stainless steels, Image processing, Mechanical properties, Medical image processing, Pattern recognition, Stainless steel
Como citar
Computational Vision and Medical Image Processing, Proceedings of VipIMAGE 2011 - 3rd ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, p. 161-166.