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Textural, Microstructural and Chemical Characterization of Ferritic Stainless Steel Affected by the Gold Dust Defect

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URI: http://hdl.handle.net/10498/32043

DOI: 10.3390/MA16051825

ISSN: 1996-1944

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2023_0331.pdf (4.310Mb)
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Author/s
Amaya Dolores, BeatrizAuthority UCA; Ruiz Flores, Andrés; Núñez Galindo, Andrés; Calvino Gámez, José JuanAuthority UCA; Almagro Bello, Juan F.; Lajaunie, Luc Cyrille JacquesAuthority UCA
Date
2023
Department
Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica
Source
Materials - 2023, Vol. 16 n.5 pp. 1-14
Abstract
The “gold dust defect” (GDD) appears at the surface of ferritic stainless steels (FSS) and degrades their appearance. Previous research showed that this defect might be related to intergranular corrosion and that the addition of aluminium improves surface quality. However, the nature and origin of this defect are not properly understood yet. In this study, we performed detailed electron backscatter diffraction analyses and advanced monochromated electron energy-loss spectroscopy experiments combined with machine-learning analyses in order to extract a wealth of information on the GDD. Our results show that the GDD leads to strong textural, chemical, and microstructural heterogeneities. In particular, the surface of affected samples presents an -fibre texture which is characteristic of poorly recrystallised FSS. It is associated with a specific microstructure in which elongated grains are separated from the matrix by cracks. The edges of the cracks are rich in chromium oxides and MnCr2O4 spinel. In addition, the surface of the affected samples presents a heterogeneous passive layer, in contrast with the surface of unaffected samples, which shows a thicker and continuous passive layer. The quality of the passive layer is improved with the addition of aluminium, explaining the better resistance to the GDD.
Subjects
ferritic stainless steel; AISI 430; grains texture; electron backscatter diffraction (EBSD); microstructure; transmission electron microscopy (TEM); monochromated electron energy-loss spectroscopy (EELS); oxidations states; machine learning; k-means clustering
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
This work is under a Creative Commons License Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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