Classification of induced magnetic field signals for the microstructural characterization of sigma phase in duplex stainless steels
| Autor: | Leandro Balby Marinho, João Paulo Viana Leite, Edgard de Macedo Silva, Walter M. L. Fialho, Victor Hugo C. de Albuquerque, Pedro Pedrosa Rebouças Filho, Josinaldo Pereira Leite, João Manuel R. S. Tavares |
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| Přispěvatelé: | Faculdade de Engenharia |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Artificial Neural Network
lcsh:TN1-997 Diffraction Bayes Toughness Support Vector Machine Materials science Scanning electron microscope Charpy impact test Duplex (telecommunications) 02 engineering and technology Corrosion Optimum Path Forest microstructural characterization Phase (matter) 0202 electrical engineering electronic engineering information engineering General Materials Science lcsh:Mining engineering. Metallurgy signal classification Metallurgy Metals and Alloys 021001 nanoscience & nanotechnology Microstructure 020201 artificial intelligence & image processing 0210 nano-technology |
| Zdroj: | Metals, Vol 6, Iss 7, p 164 (2016) Metals; Volume 6; Issue 7; Pages: 164 Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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