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Metals 2016, 6(7), 164; doi:10.3390/met6070164

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

1
Instituto Federal de Educação, Ciência e Tecnologia da Paraíba, Rua José Américo de Almeida, 707-Nordeste I, Guarabira, João Pessoa-PB 58200-000, Brazil
2
Programa de Pós-Graduação em Ciências da Computação, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Av. Parque Central-Distrito Industrial I, Maracanaú-CE 61939-140, Brazil
3
Programa de Pós-Graduação em Informática Aplicada, Universidade de Fortaleza, Av. Washington Soares, 1321, Edson Queiroz, Fortaleza-CE 60811-905, Brazil
4
Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto 4200-465, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Hugo F. Lopez
Received: 7 May 2016 / Revised: 23 June 2016 / Accepted: 7 July 2016 / Published: 14 July 2016
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Abstract

Duplex stainless steels present excellent mechanical and corrosion resistance properties. However, when heat treated at temperatures above 600 C, the undesirable tertiary sigma phase is formed. This phase presents high hardness, around 900 HV, and it is rich in chromium, the material toughness being compromised when the amount of this phase is not less than 4%. This work aimed to develop a solution for the detection of this phase in duplex stainless steels through the computational classification of induced magnetic field signals. The proposed solution is based on an Optimum Path Forest classifier, which was revealed to be more robust and effective than Bayes, Artificial Neural Network and Support Vector Machine based classifiers. The induced magnetic field was produced by the interaction between an applied external field and the microstructure. Samples of the 2205 duplex stainless steel were thermal aged in order to obtain different amounts of sigma phases (up to 18% in content). The obtained classification results were compared against the ones obtained by Charpy impact energy test, amount of sigma phase, and analysis of the fracture surface by scanning electron microscopy and X-ray diffraction. The proposed solution achieved a classification accuracy superior to 95% and was revealed to be robust to signal noise, being therefore a valid testing tool to be used in this domain. View Full-Text
Keywords: microstructural characterization; signal classification; Optimum Path Forest; Bayes; Artificial Neural Network; Support Vector Machine microstructural characterization; signal classification; Optimum Path Forest; Bayes; Artificial Neural Network; Support Vector Machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Silva, E.M.; Marinho, L.B.; Filho, P.P.R.; Leite, J.P.; Leite, J.P.; Fialho, W.M.L.; de Albuquerque, V.H.C.; Tavares, J.M.R.S. Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels. Metals 2016, 6, 164.

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