Classification of induced magnetic field signals for the microstructural characterization of sigma phase in duplex stainless steels

Abstract

Duplex stainless steels present excellent mechanical and corrosion resistance properties.However, when heat treated at temperatures above 600 ºC, the undesirable tertiary sigma phaseis formed. This phase presents high hardness, around 900 HV, and it is rich in chromium, thematerial toughness being compromised when the amount of this phase is not less than 4%. Thiswork aimed to develop a solution for the detection of this phase in duplex stainless steels throughthe computational classification of induced magnetic field signals. The proposed solution is based onan 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 magneticfield 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 ofsigma phases (up to 18% in content). The obtained classification results were compared against theones obtained by Charpy impact energy test, amount of sigma phase, and analysis of the fracturesurface by scanning electron microscopy and X-ray diffraction. The proposed solution achieved aclassification accuracy superior to 95% and was revealed to be robust to signal noise, being thereforea valid testing tool to be used in this domain