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.
Highlights
Duplex stainless steels (DSS) present excellent mechanical and corrosion resistance properties when the steel microstructures are only composed by austenite (γ) and ferrite (α) in approximately equal amounts [1,2]
The use of techniques of digital signal processing and machine learning has been quite common in engineering to tackle numerous and diverse applications [33,34,35,36,37]. In line with such applications, this work aimed to develop a solution for the automated detection of sigma phase in duplex stainless steels based on the processing and classification of signals from induced magnetic fields
We present the results obtained by the computer classifiers under comparison when applied in the classification of induced magnetic field signals in order to identify sigma phase in a duplex stainless steel
Summary
Duplex stainless steels (DSS) present excellent mechanical and corrosion resistance properties when the steel microstructures are only composed by austenite (γ) and ferrite (α) in approximately equal amounts [1,2]. These materials have been widely used in marine and petrochemical industries, desalination services and paper mills, just to cite a few examples [3,4,5]. 600 and 950 oC and after cooling from high temperatures, which typically occurs, for example, in the heat-affected zone during welding operations. The precipitation of 1.3% of sigma phase decreases the impact toughness around 7% from the solute treated condition relative to the aged one at 800 ◦ C for 10 min [4,7,16]
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