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This study investigated the influence of printing parameters and strategies on the morphological characteristics of austenitic stainless steel beads deposited on carbon steel substrates, using plasma directed energy deposition (DED). The experimental setup varied the welding current, wire feed speed, and torch travel speed, and we analyzed three printing strategies: simple-linear, overlapping, and oscillating. Moreover, advanced 3D scanning and computational analysis were used to assess the key morphological features, including bead width and height. The results showed that the computational model developed by using parabolic assumptions accurately predicted the geometric outcomes of the overlapping beads. The oscillating printing strategy was the one that showed improved morphological uniformity and bead substrate wettability, so these features were used for multi-layer component manufacturing. The use of equivalent wavelength–amplitude values resulted in maximum combinations of bead height and width. Moreover, cost-effective carbon steel substrates were feasibly used in microstructural and elemental analyses, with the latter ones confirming the alignment of the bead composition with the wire-fed material. Overall, this study provides practical insights for optimizing plasma DED processes, thus enhancing the efficiency and quality of metal component manufacturing.

This study introduces a Multiple Linear Regression (MLR) model that functions as a virtual sensor for estimating the strain-hardening rate of austenitic stainless steels, represented by the Hardening Rate of Hot rolled and annealed Stainless steel sheet (HRHS) parameter. The model correlates tensile strength (Rm) with cold thickness reduction and chemical composition, evidencing a robust linear relationship with an R-coefficient above 0.9800 for most samples. Key variables influencing the HRHS value include Cr, Mo, Si, Ni, and Nb, with the MLR model achieving a correlation coefficient of 0.9983. The Leave-One-Out Cross-Validation confirms the model’s generalization for test examples, consistently yielding high R-values and low mean squared errors. Additionally, a simplified HRHS version is proposed for instances where complete chemical analyses are not feasible, offering a practical alternative with minimal error increase. The research demonstrates the potential of linear regression as a virtual sensor linking cold strain hardening to chemical composition, providing a cost-effective tool for assessing strain hardening behaviour across various austenitic grades. The HRHS parameter significantly aids in the understanding and optimization of steel behaviour during cold forming, offering valuable insights for the design of new steel grades and processing conditions.

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.

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.