This article reports on the relation between the surface topography and the optical reflectance, both total and diffuse, of different samples of AISI 430 ferritic stainless steel. Gaussian filters with different cutoff wavelengths were applied to the height maps of the surface topography of the samples, to separate the different scales of surface roughness involved in optical scattering in the visible range of the spectrum. Significant anisotropy, related to the rolling process, was found in the topography. An effective roughness slope parameter was defined from the dependence of the ratio between the root mean square height and the autocorrelation length on the cutoff wavelength. This roughness slope demonstrated an exceptionally good linear relationship with CIE 1931 luminance, which was calculated from the diffuse reflection spectra. The color uniformity of the samples was analyzed based on their CIE L*a*b* coordinates under daylight and LED illumination. The results confirmed the strong influence of manufacturing process on the surface characteristics of AISI 430 ferritic stainless steel sheet products with a bright finish.

Localised-in-the-edge oxidation of four AISI 430 alloys was investigated after an industrial bright annealing process. The oxidised surface of each specimen was characterised by X-ray photoelectron spectroscopy (XPS), Raman-spectroscopy and SEM. The results showed that the selective oxidation of Cr and Mn took place at the coil edges of AISI 430. This led to the formation of an oxide scale based on Cr₂O₃ and MnCr₂O₄. On the other hand, the formation of Cr(OH)₃ and MnOOH in the outer part of the oxide scale was related to the effect of the H₂–N₂ environment on the annealing furnace. The results concluded that the composition of Cr and Mn in the alloy determined the composition of the oxidation products. However, the effect of annealing time was minor in this oxidation mechanism, which slightly contributed to an increase in the cation diffusion from the steel to the oxide. Finally, the results obtained indicate that the colouration of the coil edges displayed in each studied material varies according to the alloy chemical composition and annealing time.

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.

EN 1.4016 stainless steels combine good corrosion resistance with good formability and ductility. As such, their most popular applications are related to sheet forming. During re-crystallisation of Fe-Cr steels, deviations from the desired γ-fibre (gamma fibre, ||ND) texture promote a decrease in deep drawability. Additionally, α-fibre (alpha fibre, ||RD) has been found to be damaging to formability. In this study, an EN 1.4016 basic material and a modified one with optimised settings as regards to chemical composition and manufacturing process, in order to improve the formability properties, are characterised. The phase diagram, microstructure, Lankford coefficients and Electron Backscatter Diffraction (EBSD) (results confirm the evolution of texture during the processing of ferritic stainless steel. Texture is analysed by the interpretation of Orientation Distribution Function (ODF), using orientation density results for each sample obtained in the processing route. The cube ({001} ) and rotated cube ({001} ) textures dominate the crystal orientation from the slab until the intermediate annealing stage. After final annealing, there is a texture evolution in both materials; the γ-fibre component dominates the texture, which is much more intense in modified material supported by components that show good deep drawability, {554} , and good transition from hot to cold rolling, {332} . The modified composition and process material delivers a better re-crystallisation status and, therefore, the best drawability performance.

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.

Ferritic stainless steel (FSS) is widely used to manufacture deep-drawn products for corrosion resistance applications, being the alloy drawability strongly affected by its microstructural anisotropy. This study combines a variety of microscopy techniques enabling in-depth analyses of the microstructural evolution of two different FSSs correlated to their deep drawing performance. One of the steels has a good correspondence with the standard EN-1.4016 (AISI 430). The other is a modified version of the previous one with higher contents of the ferrite-stabilising elements Si and Cr, and lower contents of the austenite-stabilising elements C, N, and Mn. Electron Backscatter Diffraction results confirm that the microstructural properties and drawability of FSS in the deep drawing process are improved in the modified steel version. Scanning transmission electron microscopy under low-angle annular dark field conditions evidences that the deformation mechanism of FSS during deep drawing follows a microstructural distortion model based on the grain size gradient and shows a variation of the deformation texture depending on the alloy composition. This work demonstrates the potential of advanced microscopy techniques for optimising the processing and design of ferritic stainless steels, with slight variations in the alloy composition, for deep drawing applications.