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Fast Automated Phase Differentiation in Industrial Stainless Steel by Combining Low-Loss EELS Experiments with Machine Learning-based Algorithms

Introduction. Duplex stainless steels (DSSs) constitute a family of steels made of chromium-nickelmolybdenum-iron bi-phased alloys in which α ferrite and γ austenite fractions are present in relatively large separate volumes. Due to their bi-phased microstructure, they possess higher mechanical strength and better corrosion resistance than standard austenitic stainless steels and are used for a wide range of applications including thermal desalination plants, pipes and storage tanks for the oil & gas industry [1]. However, during aging, a large variety of phases, including the Cr-Mo-rich σ phase which is often observed at the α/γ interface boundary, are known to precipitate in DSS and lead to a dramatic deterioration of their mechanical and corrosion properties [2]. Characterizing and mapping in a timely manner the phases present in aged DSS for industrial applications if thus of high-importance. Because of the high intensity of the signal, low-loss EELS allows us to obtain large dataset with short acquisition time. However, interpretation and analysis of such data is not straightforward. Low-loss EELS spectra contain many excitation processes including volume plasmon which can be used for fingerprinting approaches but requires to use a catalogue of reference spectra and laborious fitting procedures [3]. In the present work, we developed a new and fast method based on low-loss EELS experiments to automatically separate the phases present in as-cast and aged industrial DSS. It allows us, not only to map α and γ phases, but also intermetallic phases such as the σ phase.