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

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 α-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.

A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels.

Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread processes for manufacturing flat stainless steel products, the prediction of their strain-hardening mechanical properties is of great importance to materials engineering. This work uses artificial neural networks (ANNs) to forecast the mechanical properties of the stainless steel as a function of the chemical composition and the applied cold thickness reduction. Multiple linear regression (MLR) is also used as a benchmark model. To achieve this, both traditional and new-generation austenitic, ferritic, and duplex stainless steel sheets are cold-rolled at a laboratory scale with different thickness reductions after the industrial intermediate annealing stage. Subsequently, the mechanical properties of the cold-rolled sheets are determined by tensile tests, and the experimental cold-rolling curves are drawn based on those results. A database is created from these curves to generate a model applying machine learning techniques to predict the values of the tensile strength (Rm), yield strength (Rp), hardness (H), and elongation (A) based on the chemical composition and the applied cold thickness reduction. These models can be used as supporting tools for designing and developing new stainless steel grades and/or adjusting cold-forming processes.

Analysis of chemical changes and microstructure characterization during deformation in ferritic stainless steel.

Uni- and biaxial tension deformation tests, with different degrees of deformation, have been done on AISI 430 (EN 1.4016) ferritic stainless steel samples, which had both different chemical compositions and had undergone different annealing treatments. The initial and deformed materials were characterized by using electron backscatter diffraction and backscatter electron imaging in a scanning electron microscope together with electron probe microanalysis. The correlation observed among the chemical compositions, annealing treatment, and strain level obtained after deformation is discussed.