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

• Autor(a) principal: Victor H. C. Albuquerque
• Data de Publicação: 2011
• Outros Autores: Rodrigo Y. M. Nakamura, João P. Papa, Cleiton C. Silva, João Manuel R. S.Tavares
• Tipo de documento: Livro
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/56793
• topic: Ciências Tecnológicas, Outras ciências da engenharia e tecnologias; Technological sciences, Other engineering and technologies

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 gama 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 gama 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.