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

• Published in: Metals (Basel ) Vol. 6; no. 7; p. 164
• Main Authors: Silva, Edgard, Marinho, Leandro, Filho, Pedro, Leite, João, Leite, Josinaldo, Fialho, Walter, de Albuquerque, Victor, Tavares, João
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2016
• Subjects: Artificial Neural Network; Artificial neural networks; Bayes; Composite functions; Corrosion resistance; Corrosion resistant steels; Decomposition; Duplex stainless steels; Fracture surfaces; Heat treatment; Impact analysis; Impact tests; Magnetic fields; Magnetic properties; Mechanical properties; microstructural characterization; Microstructure; Neural networks; Optimum Path Forest; Permeability; Robustness (mathematics); Sigma phase; Signal classification; Stainless steel; Support Vector Machine; Support vector machines; Ultrasonic imaging
• ISSN: 2075-4701; 2075-4701
• DOI: 10.3390/met6070164

Duplex stainless steels present excellent mechanical and corrosion resistance properties. However, when heat treated at temperatures above 600 [composite function] 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.